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1/15 group-hover:shadow-[0_28px_60px_-32px_rgba(0,0,0,0.28)]"><div class="grid gap-0 md:grid-cols-2"><div class="overflow-hidden rounded-none aspect-video md:aspect-auto md:h-full"><img alt="Claude vs ChatGPT for Google Ads: Honest 2026 Comparison" loading="lazy" width="1200" height="675" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_vs_chatgpt_google_ads_e1a220e386.png&w=1200&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_vs_chatgpt_google_ads_e1a220e386.png&w=3840&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_vs_chatgpt_google_ads_e1a220e386.png&w=3840&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-col justify-center p-8 md:p-12"><div class="flex items-center gap-2"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">ChatGPT</span><span class="text-xs text-muted-foreground">Featured</span></div><h2 class="mt-4 font-heading text-3xl font-bold tracking-tight sm:text-4xl">Claude vs ChatGPT for Google Ads: Honest 2026 Comparison</h2><p class="mt-4 text-base leading-relaxed text-muted-foreground">We support both, so we can be honest. Where Claude wins, where ChatGPT wins, and which to pick for audits, ad copy, analysis, and client reporting.</p><div class="mt-6 flex flex-wrap items-center gap-x-4 gap-y-1 text-sm text-muted-foreground"><span class="font-medium text-foreground">Philip Mohr</span><span>Oct 2, 2026</span><span>10<!-- --> <!-- -->min read</span></div><span class="mt-6 inline-flex items-center gap-1.5 text-sm font-semibold text-foreground">Read article<!-- --> <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-arrow-right h-4 w-4 transition-transform duration-300 ease-out group-hover:translate-x-0.5" aria-hidden="true"><path d="M5 12h14"></path><path d="m12 5 7 7-7 7"></path></svg></span></div></div></div></a></div></div></section><section class="mt-12 border-t border-border/60 bg-white py-16"><div class="mx-auto max-w-7xl px-6"><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0 mb-10 flex flex-wrap items-center gap-2"><button type="button" aria-pressed="true" class="group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! [a]:hover:bg-secondary/80 cursor-pointer rounded-full px-4 py-1.5 text-xs font-medium transition-colors bg-foreground text-background hover:bg-foreground
1/90">All</button><button type="button" aria-pressed="false" class="group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 cursor-pointer rounded-full px-4 py-1.5 text-xs font-medium transition-colors hover:bg-secondary/80">Google Ads</button><button type="button" aria-pressed="false" class="group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 cursor-pointer rounded-full px-4 py-1.5 text-xs font-medium transition-colors hover:bg-secondary/80">AdTech</button><button type="button" aria-pressed="false" class="group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 cursor-pointer rounded-full px-4 py-1.5 text-xs font-medium transition-colors hover:bg-secondary/80">How-To</button></div><div class="card-grid grid gap-5 sm:grid-cols-2 lg:grid-cols-3"><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/manage-ads-for-free-manus-hypd"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="Manage your ads for free: Google (and Meta) Ads inside Manus with HYPD" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_Chat_GPT_Image_Jul_14_2026_11_05_16_PM_96dad0bf18.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_Chat_GPT_Image_Jul_14_2026_11_05_16_PM_96dad0bf18.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_Chat_GPT_Image_Jul_14_2026_11_05_16_PM_96dad0bf18.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">Google Ads</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">Manage your ads for free: Google (and Meta) Ads inside Manus with HYPD</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">Run Google Ads and Meta Ads from a free AI chatbot. Manus gives 300 credits a day, HYPD is free for one account. Free AI for PPC Marketers, on a free MCP.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jul 16, 2026</span><span>6<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/claude-performance-marketing-agency-playbook"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="Claude for Performance Marketing: The Agency Playbook (2026)" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_performance_marketing_agency_playbook_0b70000030.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_performance_marketing_agency_playbook_0b70000030.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_performance_marketing_agency_playbook_0b70000030.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">AdTech</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">Claude for Performance Marketing: The Agency Playbook (2026)</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">The end-to-end agency workflow for running Google, Meta, and TikTok ads with Claude: research, build, analyze, optimize, report. Safely, across clients.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 25, 2026</span><span>14<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/ai-google-ads-audit"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="AI Google Ads Audit with Claude in 30 Minutes" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_ai_google_ads_audit_c1e43939dc.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_ai_google_ads_audit_c1e43939dc.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_ai_google_ads_audit_c1e43939dc.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">Google Ads</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">AI Google Ads Audit with Claude in 30 Minutes</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">A repeatable AI audit checklist for Google Ads: wasted spend, search terms, structure, bidding, creative, and tracking. With the exact prompts to run.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 23, 2026</span><span>11<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/claude-prompts-for-google-ads"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="15 Claude Prompts for Google Ads Agencies Actually Use" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_prompts_for_google_ads_874ebe2093.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_prompts_for_google_ads_874ebe2093.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_claude_prompts_for_google_ads_874ebe2093.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">Google Ads</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">15 Claude Prompts for Google Ads Agencies Actually Use</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">Copy-paste Claude prompts for Google Ads audits, wasted spend, ad copy, bidding, and client reports. Grouped by workflow, tested on live accounts.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 22, 2026</span><span>13<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/how-we-brought-chatgpt-ads-to-claude"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="How We Brought ChatGPT Ads to Claude" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_how_we_brought_chatgpt_ads_to_claude_3_5051fde0a8.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_how_we_brought_chatgpt_ads_to_claude_3_5051fde0a8.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_how_we_brought_chatgpt_ads_to_claude_3_5051fde0a8.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">AdTech</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">How We Brought ChatGPT Ads to Claude</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">How we built an MCP server to use ChatGPT Ads from inside Claude.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 19, 2026</span><span>5<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/connect-claude-to-google-ads-safely"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="How to Connect Claude to Google Ads Safely (Agency Guide 2026)" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_connect_claude_to_google_ads_safely_50610329c3.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_connect_claude_to_google_ads_safely_50610329c3.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_connect_claude_to_google_ads_safely_50610329c3.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">How-To</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">How to Connect Claude to Google Ads Safely (Agency Guide 2026)</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">Three ways to connect Claude to Google Ads, what each exposes, and how agencies do it safely with permissions, audit trails, and GDPR compliance.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Philip Mohr</span><span>Jun 18, 2026</span><span>10<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/on-agents-adtech-and-genai"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="On Agents, AdTech, and GenAI" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_on_agents_adtech_and_genai_3_5c5e176fd6.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_on_agents_adtech_and_genai_3_5c5e176fd6.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_on_agents_adtech_and_genai_3_5c5e176fd6.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">AdTech</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">On Agents, AdTech, and GenAI</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">On Scope3, contextual agents, and what GenAI actually changes for adtech.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 17, 2026</span><span>4<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/genai-x-marketing-landscape-ausgabe-2025"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="GenAI x Marketing Landscape - 2025 edition" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_genai_x_marketing_landscape_2025_edition_2_fed98f389d.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_genai_x_marketing_landscape_2025_edition_2_fed98f389d.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_genai_x_marketing_landscape_2025_edition_2_fed98f389d.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">AdTech</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">GenAI x Marketing Landscape - 2025 edition</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">The 2025 GenAI Ã Marketing landscape: what changed, what shipped, what's next.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 16, 2026</span><span>3<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/allocation-and-delivery-in-agentic-advertising"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="Allocation and Delivery in Agentic Advertising" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_allocation_and_delivery_in_agentic_advertising_4_bf58709c14.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_allocation_and_delivery_in_agentic_advertising_4_bf58709c14.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_allocation_and_delivery_in_agentic_advertising_4_bf58709c14.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">AdTech</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">
1Allocation and Delivery in Agentic Advertising</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">Agentic advertising has two wedges: allocation and delivery. Why both matter.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 15, 2026</span><span>3<!-- --> <!-- -->min</span></div></div></div></a></div><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><a class="group block h-full" href="/resources/blog/building-ai-agents-for-advertising"><div data-slot="card" data-size="default" class="group/card flex flex-col rounded-xl py-4 text-sm text-card-foreground ring-1 ring-foreground/10 has-data-[slot=card-footer]:pb-0 has-[>img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl h-full gap-0 overflow-hidden border-border/70 bg-card/80 pt-0 transition-all duration-300 ease-out group-hover:-translate-y-1 group-hover:border-foreground
1/15 group-hover:shadow-[0_24px_50px_-30px_rgba(0,0,0,0.25)]"><div class="aspect-video overflow-hidden"><img alt="Building AI Agents for Advertising" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="h-full w-full object-cover transition-transform duration-[600ms] ease-out group-hover:scale-[1.06]" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_building_ai_agents_for_advertising_3_7eac35de75.png&w=828&q=75 1x, /_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_building_ai_agents_for_advertising_3_7eac35de75.png&w=1920&q=75 2x" src="/_next/image?url=https%3A%2F%2Fsacred-passion-a26fbf4146.media.strapiapp.com%2Flarge_building_ai_agents_for_advertising_3_7eac35de75.png&w=1920&q=75"/></div><div data-slot="card-content" class="group-data-[size=sm]/card:px-3 flex flex-1 flex-col p-6"><span data-slot="badge" data-variant="secondary" class="group/badge inline-flex h-5 shrink-0 items-center justify-center gap-1 overflow-hidden border border-transparent whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3! bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80 w-fit rounded-full px-2.5 py-0.5 text-[10px] font-semibold uppercase tracking-wider">AdTech</span><h3 class="mt-3 font-heading text-xl font-semibold tracking-tight">Building AI Agents for Advertising</h3><p class="mt-2 line-clamp-2 text-sm text-muted-foreground">Notes on the Mastra book â prompts, RAG, evals â from building HYPD.</p><div class="mt-5 flex flex-wrap items-center gap-x-3 gap-y-1 text-xs text-muted-foreground"><span class="font-medium text-foreground">Ionut Ciobotaru</span><span>Jun 4, 2026</span><span>14<!-- --> <!-- -->min</span></div></div></div></a></div></div></div></section></main><section class="relative" style="background:linear-gradient(to bottom, #ffffff 0%, color-mix(in oklch, var(--brand-amber) 12%, #ffffff) 22%, color-mix(in oklch, var(--brand-amber) 35%, #ffffff) 50%, color-mix(in oklch, var(--brand-amber) 65%, #ffffff) 75%, var(--brand-amber) 100%)"><div class="mx-auto max-w-7xl px-6 pt-24 pb-16 text-center"><div style="transition-delay:0ms" class="transition-all duration-700 ease-[cubic-bezier(0.22,1,0.36,1)] will-change-[opacity,transform] translate-y-4 opacity-0"><h2 class="font-heading text-5xl font-bold leading-[1.05] tracking-[-0.025em] text-foreground sm:text-6xl">Bring paid media<!-- --> <span class="block">into your AI, safely.</span></h2><p class="mx-auto mt-6 max-w-2xl text-lg text-foreground/70">Join 10,000+ marketers who connected their stack to Claude and ChatGPT through HYPD.</p><div class="mt-10 flex flex-wrap items-center justify-center gap-3"><a href="https://app.hypd.ai/login?redirect_url=https%3A%2F%2Fapp.hypd.ai%2Fchat%3Flang%3Den&lang=en&ref=website&ref_placement=blog" target="_blank" rel="noopener noreferrer" type="button" tabindex="0" data-slot="button" class="group/button inline-flex shrink-0 items-center justify-center border border-transparent bg-clip-padding font-medium whitespace-nowrap transition-all outline-none select-none focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 active:not-aria-[haspopup]:translate-y-px disabled:pointer-events-none disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-3 aria-invalid:ring-destructive/20 dark:aria-invalid:border-destructive/50 dark:aria-invalid:ring-destructive/40 [&_svg]:pointer-events-none [&_svg]:shrink-0 bg-primary text-primary-foreground hover:bg-primary/80 h-12 gap-2 text-base has-data-[icon=inline-end]:pr-3.5 has-data-[icon=inline-start]:pl-3.5 [&_svg:not([class*='size-'])]:size-4 rounded-full px-8">Sign up<!-- --> <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-arrow-right" aria-hidden="true"><path d="M5 12h14"></path><path d="m12 5 7 7-7 7"></path></svg></a><button type="button" tabindex="0" data-slot="button" class="group/button inline-flex shrink-0 items-center justify-center border bg-clip-padding font-medium whitespace-nowrap transition-all outline-none select-none focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 active:not-aria-[haspopup]:translate-y-
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1<script>self.__next_f.push([1,"You can now run your Google Ads from a free AI chatbot. No Claude Code, no complicated setup. Two tools, both free: Manus for the agent, HYPD for the ads.\n\nMost people assume that connecting an ad account to an AI assistant means paying for ChatGPT or Claude first, and in practice they're right. ChatGPT won't let you add a custom connector on the free tier, and while Claude technically will, the free limits only stretch to a couple of prompts. Manus is the exception. The free plan gives you 300 credits a day, which is 20 or more prompts, and HYPD is free for one account. So what you end up with is free AI for Google Ads, and Meta is in beta now too.\n\nSo, what each one actually does.\n\n## What Manus is good at\n\nManus is a general-purpose AI agent. You give it a task and it runs it, across multiple steps, instead of just answering you.\n\n- Runs multi-step tasks on its own: research, analysis, pulling the data, building the output\n- Browses the web, works with files, and hands back a report or a table\n- Connects to outside tools through connectors, so it can work on your real accounts\n- Projects, skills and scheduled tasks, so a repeated workflow isn't a re-typed prompt\n- Free tier with 300 credits a day, which is enough to actually use it\n\n## What HYPD is good at\n\nHYPD is the ads layer. On its own it's a read-only co-pilot for your ad accounts. Attached to Manus, it's the part that knows advertising.\n\n- Connects Google Ads (live) and Meta Ads (beta) to any chatbot as an MCP connector\n- Ships with skills and prompts out of the box, so you're not writing GAQL or guessing at the right questions\n- Account overviews, campaign analysis, keyword research, budget and waste checks, audits\n- Reads the account through the official APIs and answers in plain language\n- Read-only by default. Write actions ask for your approval first\n- Free for one account, and that account can be Google or Meta\n\nThat last point matters. HYPD is the reason Google Ads works inside Manus at all. Manus brings the agent, HYPD brings the ads.\n\n## What you can do together\n\nConnect an account once in HYPD, attach the connector in Manus, then ask in plain language:\n\n- \"Using the HYPD connector, show my top campaigns by spend last week.\"\n- \"Where is budget being wasted across my accounts?\"\n- \"Find new keyword opportunities for my best campaign.\"\n- \"Which ad accounts am I connected to?\"\n\nIt reads the account and answers. No dashboard to open, no export step.\n\n## Set it up in about four minutes\n\n**Part A: connect an account in HYPD**\n\n1. Sign in at app.hypd.ai.\n\n\n\n2. Go to Sources and connect your ad account. Google Ads connects with a Google login, Meta connects through Facebook Login for Business.\n\n\n\n3. Approve the requested access. Back in HYPD, the account shows connected.\n\n\n\n**Part B: run it in Manus**\n\n4. Sign in at manus.im.\n\n\n\n5. Attach the HYPD connector to your project.\n\n\n\n6. Ask a question. Manus calls HYPD's tools, reads the account, and returns live data.\n\n\n\nThe same works for Google Ads. Ask for last week's performance, wasted spend, or keyword ideas, and HYPD runs the query and answers.\n\n\n\nOne thing worth pointing out, since it's visible in the screenshots: the free plan runs Manus 1.6 Lite. It's a smaller model than the flagship, but with the right skills and tools attached it handles this work fine. Everything above was done on Lite.\n\n## Why not just use Manus's own tools, or a raw MCP?\n\nYou can point a general agent at an ad platform and get raw API access, but you get no opinion with it. You still have to know which questions are worth asking, how to ask them, and what a bad number looks like.\n\nHYPD is a free MCP built specifically for ads, so that part comes with it:\n\n- **Pre-made prompts and workflows** for the things you actually do: audits, wasted spend, keyword research, competitor checks\n- **Skills** you can reuse instead of rebuilding a prompt every time\n- **Access to our knowledge system**, so the answers are grounded in how paid search actually works, not just whatever the model recalls\n- **Read-only guardrails** by default, and one connector covering both Google and Meta\n\nLess setup, and nothing changes in your account unless you say so.\n\n## Manus vs ChatGPT vs Claude\n\nHYPD runs as a connector, so the host chatbot decides what you can actually do with the data. The workflows people get the most out of are the rich ones: live \"data studio-like\" dashboards that pull fresh numbers every time you open them, landing page audits turned into prioritised fixes, campaign breakdowns you can send to a client.\n\nFile generation is not the dividing line. Claude creates Excel, PowerPoint and PDF files on every plan, including the free one, and ChatGPT's free tier can produce files too, with limits. Where the plans actually bite is the connector and the live artifacts.\n\nChatGPT needs a paid plan. Custom connectors work from Plus upwards, and HYPD runs fine there, but there is nothing on the free tier.\n\nClaude is more open on paper. A free account can add one custom connector, so HYPD does work. The free limits are the constraint: in our testing, a free account gets through a couple of prompts before it hits them. Live artifacts, the dashboards that pull fresh numbers every time you open them, are on the paid plans only.\n\nManus runs the connector on the free plan. In our testing, 300 credits a day comes out to 20 or more prompts, which is enough to actually work with.\n\n| | Manus | ChatGPT | Claude |\n|---|---|---|---|\n| Custom MCP connector on free | Yes | No, needs Plus or above | Yes, one connector |\n| Prompts a day on the free tier | 20+ (300 credits) | Not available | A couple |\n| File exports (PPTX / XLSX / PDF) | Yes | Yes, limited on free | Yes, on all plans |\n| Live dashboards / interactive artifacts | Yes | Paid | Paid plans only |\n| Cost to do real work | Free | Subscription | Subscription in practice |\n\nThis isn't a claim that Manus beats Claude or ChatGPT at the work. Both are excellent, and most of our tutorials still run on Claude. It's that Manus is the one you can do it on for free.\n\n## What it costs: nothing to start\n\n- **Manus:** free plan, 300 credits a day, which is 20 or more prompts.\n- **HYPD:** free for one account (Google or Meta).\
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1<script>self.__next_f.push([1,"Most guides on Claude for performance marketing are written for one person with one ad account. They show you a clever prompt, a screenshot of a tidy analysis, and call it a workflow.\n\nIf you run an agency or a freelance roster, your reality looks different. You manage eight, fifteen, maybe forty client accounts across Google, Meta, and TikTok. Every account has its own goals, its own brand voice, its own sensitivities about data. You need workflows that repeat reliably across clients, not party tricks. And at the end of the month, you need to show each client what they got for their money.\n\nThis playbook covers that reality: the full Claude performance marketing playbook for agencies and freelancers, from research through reporting, with the guardrails that make it safe to run across a client roster.\n\n## Why Claude for performance marketing\n\nThere are plenty of AI tools. Here is why Claude specifically earns a place in a paid media workflow, without the fanboying.\n\n**It reasons well over messy data.** Ad account data is rarely clean. Campaigns with inconsistent naming, conversion actions that changed mid-quarter, a Performance Max campaign muddying your attribution. Claude handles this kind of ambiguity better than most tools: it asks what a column means instead of guessing, and it flags when two data sources disagree.\n\n**Long context fits whole accounts.** You can give Claude a full campaign export, twelve months of performance data, search term reports, and the client brief in one conversation. It holds all of it at once, which is what account-level analysis actually requires. Spot checks on isolated campaigns miss the structural problems.\n\n**It follows multi-step instructions.** A real audit is a sequence: check conversion tracking first, then structure, then query waste, then budget allocation. Claude works through ordered procedures without losing the thread, which means you can encode your agency's methodology once and reuse it.\n\n**It is honest about uncertainty.** When the data does not support a conclusion, Claude tends to say so rather than inventing a confident answer. In a discipline where a wrong call costs real client budget, that trait matters more than eloquence.\n\nNone of this makes Claude a media buyer. It makes Claude a very fast, very thorough analyst that works at your direction. The rest of this playbook is built on that division of labor.\n\n## Two ways to work with Claude\n\nBefore the workflow, a structural decision: how does Claude see your account data?\n\n**Option 1: manual exports.** Download reports from Google Ads, Meta Ads Manager, or TikTok Ads Manager as CSVs and upload them to a Claude conversation. This works, and it is how most people start. The downsides show up at agency scale: exports go stale the moment you download them, every analysis starts with ten minutes of export-and-upload, follow-up questions need new exports, and you end up with client data scattered across downloads folders and chat histories.\n\n**Option 2: a live, permissioned connection.** Claude queries the ad platforms directly, pulls exactly the data a question requires, and always works from current numbers. Follow-up questions take seconds instead of another export cycle. Done properly, the connection is read-only by default, scoped per account, and logged.\n\nThe workflow below assumes a live connection, because at roster scale the export approach collapses under its own friction. If you have not set this up yet, start with our guide on [how to connect Claude to Google Ads safely](/resources/blog/connect-claude-to-google-ads-safely).\n\n## The five-stage AI paid media workflow\n\nThis is the heart of the playbook. Five stages, in the order client work actually flows: research, build, analyze, optimize, report. For each stage you get what it looks like in practice, example prompts, channel notes, and a realistic sense of the time saved. The time figures are ranges from practice, not citations from a study, and your mileage depends on account complexity.\n\n### Stage 1: Research\n\nResearch is where new client engagements start and where existing accounts go to find their next growth lever. Claude compresses the grunt work.\n\n**In practice:** competitor angle research from landing pages and ad copy you feed it, keyword expansion that combines seed terms with what the account's search term data already proves converts, audience hypotheses for Meta and TikTok grounded in the client's actual customer descriptions, and structured briefs for new clients built from onboarding call notes and the account's history.\n\n**Example prompts:**\n\n``` Competitor Comparison\nHere are the landing pages of this client's three main competitors and the ad copy we scraped from the transparency libraries. Map the angles each competitor leads with, identify which angles our client is not contesting, and rank the gaps by likely commercial value.\n```\n\n``` Keyword Research\nUsing the seed terms below plus the top 50 convert
1ing search terms from this account's last 90 days, build a keyword expansion grouped by intent stage. Flag terms that look high volume but off-intent for this client's offer.\n```\n\n**Channel notes:** for Google, anchor expansion in the search terms report, not just external keyword tools, because the account's own conversion data is the strongest signal you have. For Meta, use Claude to turn customer reviews and survey verbatims into audience and angle hypotheses, then let broad targeting and creative testing do the targeting work. For TikTok, research is mostly creative research: have Claude categorize hooks and formats from top organic and paid content in the niche.\n\n**Time saved:** a competitive and keyword research package that took a strategist 4 to 6 hours typically compresses to 1 to 2, with the human time spent on judgment rather than collection.\n\n### Stage 2: Build\n\nBuilding is where most agencies quietly burn margin. Campaign shells, ad copy variants, naming, tracking parameters. All of it is structured work that Claude does well under clear instructions.\n\n**In practice:** campaign structure proposals derived from the client's goals, budget, and conversion volume (Claude is good at the unglamorous math of whether a budget can actually feed the number of campaigns you are tempted to build), RSA headline and description sets at scale that stay inside character limits and brand voice, Meta primary text and hook variants mapped to specific angles from your research stage, and enforcement of naming conventions and UTM standards that humans drift away from by Thursday.\n\n**Example prompts:**\n\n``` Campaign Structure\nClient sells B2B payroll software, 8,000 euro monthly Google budget, roughly 60 conversions a month at current rates. Propose a campaign structure that respects Smart Bidding's data thresholds. Tell me explicitly what you would NOT build at this budget and why.\n```\n\n``` Ad Copy\nHere is the brand voice guide and our top three performing RSAs. Write 4 new RSA variants for the ad group below: 15 headlines and 4 descriptions each, character limits enforced, no headline repeating a benefit another headline in the same ad already covers.\n```\n\n**Channel notes:** for Google, make Claude justify structure against conversion volume per campaign, because over-segmentation is the most common build error in agency accounts. For Meta, generate copy in batches tied to one angle per batch, so creative testing stays readable. For TikTok, use Claude for hooks and scripts, not finished creative; the platform punishes anything that reads like an ad written at a desk.\n\n**Time saved:** copy production and campaign documentation that consumed 3 to 5 hours per launch typically drops to about 1 hour of generation plus review. Review is not optional. You are still the editor of record.\n\n### Stage 3: Analyze\n\nThis is the stage that changes how the week feels. Instead of opening six dashboards and assembling a mental picture, you interrogate each account in plain language.\n\n**In practice:** a weekly account review where you ask questions and get answers grounded in live data, anomaly explanation when a metric moves and the client wants to know why before lunch, and cross-channel comparison that answers the only budget question that matters: where is the marginal euro best spent right now?\n\n**Example prompts:**\n\n``` Period Comparison\nCompare this account's last 14 days to the prior 14. Identify the three changes with the largest impact on cost per conversion, separate genuine performance shifts from tracking or seasonality effects, and tell me what you checked to rule out each alternative explanation.\n```\n\n``` Cross-Channel Budget\nAcross this client's Google, Meta, and TikTok accounts, estimate the marginal return of moving 1,000 euros from the weakest channel to the strongest. State your assumptions about attribution overlap before you give me a number.\n```\n\n**Channel notes:** on Google, anchor analysis in search terms and auction insights, where the explanations usually live. On Meta, force the distinction between creative fatigue and audience saturation; the fixes are different. On TikTok, watch the gap between platform-reported and tracked conversions, and have Claude state which number it is reasoning from.\n\nFor the full structured version of this stage, our [AI Google Ads audit checklist](/resources/blog/ai-google-ads-audit)
1walks through the complete account interrogation step by step.\n\n**Time saved:** weekly reviews of 30 to 45 minutes per account compress to 10 to 15, and the depth usually improves, because asking a follow-up question costs seconds instead of another pivot table.\n\n### Stage 4: Optimize\n\nHere is the rule that governs this entire stage: **AI proposes, the operator approves.** Claude can find the opportunity, model the impact, and draft the change. A human with account context decides whether it ships. Every workflow below ends in a recommendation, not an autonomous action.\n\n**In practice:** negative keyword mining from search term reports with proposed match types and shared list placement, budget reallocation modeling that shows expected impact before you commit, bid strategy reviews that check whether targets match what the conversion volume can support, and creative refresh decisions on Meta and TikTok triggered by fatigue signals rather than gut feel.\n\n**Example prompts:**\n\n``` Negative Keywords\nMine the last 30 days of search terms in this account for negative candidates. Group them by theme, propose match types, recommend campaign versus shared list placement, and separately flag any term you are uncertain about rather than including it. I will approve the final list.\n```\n\n``` Budget Reallocation\nModel moving 20 percent of budget from the two weakest campaigns to the two strongest. Show expected impact on total conversions, note where diminishing returns are likely to bite, and list what could make this model wrong.\n```\n\n**Channel notes:** on Google, never let proposed negatives ship without review; AI cannot reliably tell that a strange-looking query is actually the client's best customer searching in their own vocabulary. On Meta, pair every fatigue diagnosis with a concrete creative direction from your Stage 1 research, so the recommendation is actionable. On TikTok, refresh cycles run faster than on Meta; have Claude track creative age explicitly.\n\nWe keep a maintained library of our best optimization prompts in [Claude prompts for Google Ads](/resources/blog/claude-prompts-for-google-ads), copy-paste ready.\n\n**Time saved:** routine optimization passes of 2 to 3 hours per account per month typically come down to about 1 hour, nearly all of it review and approval rather than spreadsheet work.\n\n### Stage 5: Report\n\nReporting is where agencies win or lose retention, and it is the stage clients actually see. Claude's strength here is translation: turning account data into a narrative a founder or marketing director understands without a glossary.\n\n**In practice:** monthly client reports written in plain language around the metrics each client actually cares about, quarterly business reviews that connect three months of spend to a coherent story with a forward plan, and calm, evidence-based explanations when something dropped, written before the client has time to panic.\n\n**Example prompts:**\n\n``` Monthly Report\nWrite this month's client report from the data below. The client is a non-technical founder who cares about cost per qualified lead. Lead with the outcome, explain the one significant change we made and why, and keep it under 400 words. No jargon, no acronyms without explanation.\n```\n\n``` Performance Drop\nConversions dropped 18 percent this week. Draft a client-facing explanation that states what happened, what we have verified versus what we are still checking, and what we are doing about it. Calm and factual, no hedging filler.\n```\n\n**Channel notes:** in cross-channel reports, make Claude state which attribution lens each number uses, because mixing platform-reported figures across Google, Meta, and TikTok without saying so is how agencies erode trust by accident. For QBRs, feed it the previous quarter's report too, so the narrative has continuity instead of restarting from zero.\n\n**Time saved:** this is usually the biggest single win. Reporting blocks of 2 to 4 hours per client per month routinely drop to 30 to 45 minutes of generation, fact-checking, and personalization.\n\n## Where AI should not be trusted alone\n\nAn honest playbook includes the boundaries. Four areas where Claude assists but must not decide:\n\n**Strategy calls.** Whether a client should enter a new market, cut a channel entirely, or shift from lead gen to demand gen depends on business context that no ad account contains. Claude can structure the decision and stress-test your reasoning. The call is yours.\n\n**Brand judgment.** Claude follows a voice guide competently, but it cannot feel that a technically on-brief headline is somehow wrong for this client. That instinct is built from human context, and clients pay for it.\n\n**Anything irreversible.** Pausing a campaign that has been learning for weeks, deleting historical assets, overwriting conversion settings. Any action that cannot be cleanly undone gets a human review, every time, no exceptions for deadline pressure.\n\n**Client relationships.** AI drafts the difficult email. It does not send it. Trust between an agency and a client is maintained by people, and clients can tell the difference.\n\nIf you internalize one thing from this playbook: Claude raises the ceiling on how much high-quality work one operator can produce. It does not replace the operator's judgment, and the agencies that pretend otherwise will learn that on a client's budget.\n\n## Running this across a client roster\n\nEverything above works fine for one account. The problems start at account number five.\n\nContext switching: every conversation needs to know which client it concerns, what their goals are, and what their brand s
1ounds like, or you re-explain it daily. Data separation: client A's performance data must never leak into an analysis for client B, which is hard to guarantee when everything flows through ad hoc exports and shared chat threads. Permissions: a junior should be able to analyze accounts without being able to touch them, and that boundary should be enforced by the system, not by trust. Accountability: when a client asks what the AI looked at and what changed, \"we are not sure\" is not an answer an agency can give.\n\nThis is the gap HYPD was built for. HYPD sits between Claude and your ad accounts as a permission layer: client context is stored per account so every conversation starts informed, permissions are granular per client and per action (read-only by default, write access only where you explicitly grant it), every query and proposed change lands in an audit log, and the whole thing runs on EU hosting under GDPR. It turns the workflow in this playbook from something one careful person can do into something a team can run across a roster without losing sleep.\n\n## Getting started: a two-week adoption plan\n\nDo not roll this out to every client at once. Two weeks, one deliberate sequence:\n\n**Week 1: prove it on one account.** Pick a mid-complexity client, connect the account read-only (the [safe connection guide](/resources/blog/connect-claude-to-google-ads-safely) covers the setup), and run the full audit from Stage 3. Then draft that client's monthly report with the Stage 5 prompts and compare it honestly against your usual output. By Friday you know whether the quality holds, at near-zero risk, because nothing could write to the account.\n\n**Week 2: extend and roll out.** Add the build and optimize workflows on the same account, with every change passing through your approval. Document which prompts worked as your internal playbook. Then extend to the next two or three clients, adding per-client context as you go. Most teams reach the full roster within a month and wonder why reporting week used to exist.\n\n## FAQ\n\n**Is Claude better than ChatGPT for performance marketing?**\nBoth are capable. In our testing, Claude has the edge for account-level analysis: longer effective context for full account data, stronger adherence to multi-step procedures, and more reliable honesty when data is inconclusive. ChatGPT remains strong for fast creative iteration. We ran the comparison on real account work in [Claude vs ChatGPT for Google Ads](/resources/blog/claude-vs-chatgpt-google-ads).\n\n**Do I need to know how to code?**\nNo. Every workflow in this playbook runs in plain language. A live connection through a platform like HYPD is configured through an interface, not an API integration project. Comfort with reading data critically matters far more than any technical skill.\n\n**Is my client data safe?**\nIt depends entirely on how you connect, which is why this question deserves its own answer: see [is it safe to connect AI to your Google Ads account](/resources/blog/is-it-safe-connect-ai-google-ads). The short version: use read-only access by default, scope permissions per account, keep an audit trail, and never paste client data into consumer chat tools with training enabled.\n\n**Which Claude plan do I need?**\nFor agency use, a paid plan is effectively required; free tier limits will not survive a single audit session. A Pro plan handles a small roster. Teams running daily analysis across many accounts usually land on a higher-tier or team plan for the usage headroom. Start with Pro and upgrade when you hit limits, not before.\n\n**Can Claude manage my campaigns autonomously?**\nTechnically increasingly possible, practically a bad idea, and nothing in this playbook recommends it. AI proposes, the operator approves. Autonomous changes to live client budgets fail on accountability alone
1: when a client asks why spend doubled overnight, \"the AI decided\" ends the relationship. Use AI for leverage on analysis and production, and keep a human between every recommendation and the account.\n\n---\n\nReady to run this playbook across your client roster? Start your free HYPD trial and connect your first account, read-only, in minutes."])</script>
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1<script>self.__next_f.push([1,"Audits are how agencies win new clients and how freelancers prove they are worth the retainer. The problem is the time cost. A proper manual audit means pulling reports, cross-referencing campaigns, digging through search terms, and writing it all up. That is 4 to 8 hours per account, which is why most audits get rushed or skipped.\n\nAn AI Google Ads audit changes the math. With Claude connected directly to the account, the same audit takes about 30 minutes, and the quality usually goes up, because the AI does not get tired on part five and skip the budget analysis. Every section gets the same level of attention.\n\nOne thing before we start: this workflow assumes a live connection between Claude and Google Ads, not CSV exports pasted into a chat window. Exports go stale the moment you download them, and you will spend more time wrangling files than auditing. If you have not set that up yet, read [how to connect Claude to Google Ads safely](/resources/blog/connect-claude-to-google-ads-safely) first.\n\n## Before you start: what you need\n\nFour things, and the whole audit goes faster if you sort them out up front.\n\n**1. Account access, ideally read-only.** An audit is analysis, not action. You do not need write access to run one, and on a client or prospect account you actively do not want it. Read-only access means nothing can change by accident, which also makes the access conversation with a prospect much easier.\n\n**2. At least 90 days of data.** Shorter windows make seasonal noise look like trends. If the account is newer than 90 days, use what exists and say so in the report.\n\n**3. Client context.** This is the part most people skip and it is the part that separates a useful audit from a generic one. Before running any prompt, give Claude:\n\n- What the business sells and to whom\n- Margins or average order value, even rough numbers\n- Target CPA or ROAS, and whether those targets are actually achievable or aspirational\n- Anything unusual: long sales cycles, offline conversions, seasonality\n\n**4. A safe connection.** If you are auditing a client account, you are responsible for what happens in it. Use a setup with explicit permissions so the AI can read but not touch. This is exactly what HYPD's permission layer is for: you decide what the AI can see and do per account, and read-only is one toggle. More on the risks and how to handle them in [is it safe to connect AI to Google Ads](/resources/blog/connect-claude-to-google-ads-safely).\n\n## The 6-part AI Google Ads audit checklist\n\nEach part below has the reasoning, the exact prompt to run, and what good and bad look like in the output. The prompts are vendor-neutral. They work in any setup where Claude can query the account.\n\n### Part 1: Conversion tracking (5 minutes)\n\nAlways start here. If tracking is broken, every other number in the account is fiction, and every recommendation built on those numbers is wrong. There is no point optimizing toward a conversion action that double-counts or stopped firing in March.\n\n``` Conversion tracking\nReview the conversion tracking setup in this account. List every\nconversion action with its status, source, counting method, and\nattribution model. Flag:\n1. Duplicate or overlapping actions (e.g. a purchase tracked by\n both the Google tag and a GA4 import)\n2. Actions marked as primary that look like soft signals\n (page views, button clicks) rather than real conversions\n3. Anomalies in conversion volume over the last 90 days: sudden\n drops to zero, sudden spikes, or actions that have never fired\n4. Mismatches between counting method and action type (e.g.\n \"every\" on a lead form)\n```\n\n**Good output:** a clean list, one clear primary conversion per goal, stable volume, no zombie actions. You can move on in two minutes.\n\n**Bad output:** three actions all counting purchases, a primary conversion that is actually a scroll event, or a flatline since a site relaunch. If you find this, the audit just paid for itself, and you should weight every downstream finding accordingly.\n\n### Part 2: Wasted spend and search terms (5 minutes)\n\nThis is where audits win clients, because wasted spend is concrete. \"You spent 2,400 euros on searches that never converted\" lands harder than any strategic observation.\n\n``` Wasted spend and search terms\nPull search terms from the last 90 days with cost above zero and\nzero conversions. Cluster them into themes (e.g. job seekers,\nDIY/free intent, wrong location, competitor brands we don't want,\nirrelevant products). For each theme show total cost, click count,\nand 3 example terms. Then propose a negative keyword list per\ntheme, with suggested match types and whether each should be\naccount-level or campaign-level. Do not add anything, just propose.\n```\n\n**Good output:** small, scattered waste with no obvious pattern. That is
1what a well-maintained account looks like.\n\n**Bad output:** clear clusters, like a plumbing company paying for \"plumber salary\" and \"how to fix leaking tap yourself,\" with hundreds of euros per theme. The clustering matters because a list of 400 raw terms is unusable, but eight themes with costs attached is a deliverable.\n\n### Part 3: Account structure (5 minutes)\n\nStructure problems are invisible day to day and expensive over a year. Overly broad ad groups dilute ad relevance, overlapping campaigns bid against each other, and orphaned keywords sit in forgotten corners collecting spend.\n\n``` Account structure\nMap the account structure: campaigns, ad groups, keyword counts,\nand themes per ad group. Flag:\n1. Ad groups with more than 15-20 keywords or with keywords\n spanning multiple distinct intents\n2. Campaigns or ad groups that overlap on the same keywords or\n close variants, where traffic could be cannibalized\n3. Keywords with spend but no impressions in 90 days, or active\n keywords in paused/empty ad groups\n4. Naming inconsistencies that suggest the account grew without\n a plan\nSummarize as: what the structure suggests about how this account\nhas been managed.\n```\n\n**Good output:** tight ad groups with a single intent each, no overlap, consistent naming. Note it in the report; clients should hear what is working too.\n\n**Bad output:** a \"General\" ad group with 80 keywords, two campaigns bidding on the same brand terms, or a structure that clearly accreted over three different managers. That last summary line in the prompt often produces the most quotable sentence in your report.\n\n### Part 4: Bidding strategies (5 minutes)\n\nSmart bidding is only smart with enough data. A Target CPA campaign getting four conversions a month is guessing, and a manual CPC campaign with 200 conversions a month is leaving automation value on the table. The audit question is fit, not which strategy is best in the abstract.\n\n``` Bidding strategies\nFor each campaign, list the bidding strategy, the target (if any),\nand conversions over the last 90 days. Evaluate fit:\n1. Flag campaigns on Target CPA or Target ROAS with fewer than\n roughly 30 conversions in 30 days, where smart bidding is\n starved of data\n2. Flag targets that sit far from actual performance (e.g. a\n 50 euro target CPA on a campaign averaging 110)\n3. Flag high-volume campaigns still on manual or enhanced CPC\n where automation likely outperforms\nFor each flag, recommend a strategy or a consolidation that would\nfix the data problem, with reasoning.\n```\n\n**Good output:** strategies matched to volume, targets within shouting distance of reality.\n\n**Bad output:** half the account on Target ROAS with single-digit monthly conversions, or targets set as wishes rather than plans. The common fix, consolidating thin campaigns so conversion data pools, is a recommendation clients rarely hear from a dashboard.\n\n### Part 5: Ads and creative (5 minutes)\n\nCreative is where accounts quietly decay. Ads written two years ago, RSAs with the minimum asset count, pinned headlines strangling the algorithm. None of it shows up as an alert anywhere, so nobody looks.\n\n``` Ads and creative\nAudit the ads in this account:\n1. RSA ad strength per ad group, and which ad groups have only\n one active ad\n2. RSAs below the maximum headline and description counts, and\n heavy pinning that limits combinations\n3. Ads that have not been edited in over 12 months\n4. Asset-level performance: which headlines and descriptions are\n rated low, and which themes appear in the best performers\nSummarize the top 5 creative fixes by likely impact.\n```\n\n**Good output:** Good-to-Excellent ad strength, multiple ads per group, assets refreshed within the last two quarters, low-rated assets already swapped out.\n\n**Bad output:** \"Poor\" ad strength across the board, single-ad ad groups, or every headline pinned to position one. Worth noting in the report: ad strength is a directional signal, not gospel, but a wall of \"Poor\" alongside stale edit dates tells a real story.\n\n### Part 6: Budget allocation (5 minutes)\n\nThe fastest improvement in most accounts is not a new tactic, it is moving existing budget from losers to constrained winners. This analysis is tedious by hand and trivial for an AI
1with account access.\n\n``` Budget allocation\nAnalyze budget allocation over the last 90 days:\n1. Campaigns that are budget-limited (high impression share lost\n to budget) AND beating the account's average CPA or ROAS.\n These are constrained winners.\n2. Campaigns spending freely with performance below the account\n average. These are well-funded losers.\n3. Model a reallocation: if budget moved from group 2 to group 1\n at current efficiency, estimate the change in monthly\n conversions. State your assumptions and label this as an\n estimate, not a forecast.\n```\n\n**Good output:** budget roughly tracks performance, winners are not capped.\n\n**Bad output:** the best campaign in the account losing 40 percent of impression share to budget while a legacy campaign burns the same amount at twice the CPA. The modeled reallocation, clearly labeled as an estimate, is often the single most persuasive number in the entire audit.\n\nFor deeper variations on all of these prompts, see our full library of [Claude prompts for Google Ads](/resources/blog/claude-prompts-for-google-ads).\n\n## Turning the audit into a client-ready report\n\nSix analyses are not a deliverable. The last step is compiling them, and this is the prompt that saves you the writing evening.\n\n``` Client-ready report creation\nCompile the six audit sections above into a client-ready report:\n1. A one-paragraph executive summary in plain language\n2. Findings ranked by estimated impact, each with: what we found,\n why it matters, what we recommend, and rough effort to fix\n3. A \"what's working\" section, because the client should know\n what not to break\n4. A suggested 30-day action plan\nWrite for a business owner, not a PPC specialist. No jargon\nwithout a plain-language explanation. Where impact is estimated,\nsay so explicitly.\n```\n\nReview it before sending. You will usually adjust priorities based on context the AI did not have, which is exactly your job in this workflow.\n\nOne practical advantage if you run this through HYPD: every query and response from the audit is logged in an audit trail, so when a client asks \"what exactly did you look at,\" you can show them, line by line. For prospect audits, that transparency is a trust-builder you cannot fake.\n\n## What AI audits miss\n\nHonesty section, because overselling this helps nobody.\n\n**Business context it was never told.** The AI does not know the client is deliberately overpaying for brand terms because a competitor is conquesting, or that the \"underperforming\" campaign exists for a strategic partner. Garbage context in, confident nonsense out. This is why the setup step matters.\n\n**Offline conversion nuance.** If leads convert to revenue weeks later in a CRM, the in-platform numbers undercount real performance, and the AI will only know that if offline data is imported or you tell it.\n\n**Brand considerations.** Whether an ad is on-brand, whether a negative keyword theme conflicts with a planned product launch, whether the tone fits the market. Data does not carry this.\n\n**Landing page experience beyond the data.** The AI sees bounce signals and conversion rates. It does not see that the page loads a cookie wall over the CTA on mobile, unless you look.\n\nThe honest framing: the AI does the heavy lifting, every section, every time, in 30 minutes. The operator makes the calls. That division of labor is the whole point, and it is why this workflow makes good PPC people faster rather than replacing them.\n\n## FAQ\n\n**Can I do this without connecting my account?**\nPartially. You can paste CSV exports into Claude and run similar prompts, but you will spend significant time exporting, the data goes stale immediately, and cross-referencing between sections (like tracking issues affecting bidding analysis) becomes manual again. A live read-only connection is what makes the 30-minute number real.\n\n**How accurate is it?**\nOn the data layer, very: it is reading the same numbers the interface shows. On interpretation, it is as good as the context you give it. The failure mode is not wrong numbers, it is confident recommendations that ignore business context it never had. Review before you send, always.\n\n**Will the AI change anything in the account during the audit?**\nNot if you set it up correctly. Run audits with read-only permissions, and changes are technically impossible, not just unlikely. HYPD enforces this at the permission layer per account, so an audit connection literally cannot write.\n\n**How often should I run it?**\nFull six-part audit: quarterly per account, plus every new client or prospect. Parts 1 and 2 (tracking and wasted spend) are worth running monthly, since they catch problems that compound.\n\n**Can I run this across multiple client accounts?**\nYes, and this is where the workflow compounds for agencies. With per-account context and permissions in place, the same checklist runs against each account in sequence, and you can even ask for a cross-account summary of the most common issues in your book of business.\n\n## Run your first audit\n\nConnect a Google Ads account read-only with a free HYPD trial and run this checklist on a real account before the end of the day.\n"])</script>
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1<script>self.__next_f.push([1,"Most lists of Claude prompts for Google Ads start with \"act as a PPC expert with 10 years of experience.\" Skip those. A roleplay prompt with no data behind it produces advice you could have pulled from any blog post, because that is where it comes from.\n\nThe prompts below are written to run against your actual account data: real search terms, real conversion numbers, real budgets. That is where Claude stops being a writing assistant and becomes a junior analyst who never gets tired of pulling reports.\n\nThey work best with a live connection to your account. If you have not set that up, read our guide on how to [connect Claude to Google Ads safely](https://www.hypd.ai/resources/blog/connect-claude-to-google-ads-safely) first. Pasting exported reports into a chat also works, just with more friction.\n\nFive workflows, three prompts each. They are written to run as-is, with no fill-in-the-blank brackets to maintain. The rule behind all of them: if Claude is missing something it needs, it asks you for it instead of guessing. Three things worth knowing:\n\n- **Which account comes first.** Every prompt says \"your account\" and \"all campaigns.\" If you have more than one Google Ads account connected, Claude asks which client or account you mean before it pulls anything. With a single account connected, it just runs.\n- **The numbers are defaults, not rules.** Spend thresholds, impression counts, and percentages are starting points sized for a mid-size account. Change them to fit yours.\n- **Context only you have** (budget targets, brand voice, the client's goals) is asked for, never assumed. Store it once with HYPD (more on that at the end) and Claude runs straight through without asking.\n\nWant Claude to confirm everything up front? Paste this line above any prompt: \"Before you run this, ask me which Google Ads account to use and anything else you need, targets, client goals, brand voice, margins. Don't assume.\"\n\n## Account audit and health: Claude prompts for Google Ads diagnostics\n\n### 1. Full account health check\n\n```Account health check\nPull performance for all enabled campaigns for the last 30 days and compare against the prior 30 days. For each campaign show spend, conversions, cost per conversion, conversion value, ROAS, CTR, and impression share.\n\nFlag any campaign where cost per conversion moved more than 20% in either direction, or impression share dropped more than 10 points. For each flagged campaign, list the most likely causes ranked by probability, and tell me what you would check next to confirm.\n```\n\nThis returns a period-over-period table plus a shortlist of campaigns that need attention. Use it as Monday morning triage: you start with the three campaigns that actually moved. Follow-up: \"go deeper on that campaign, break the change down by device, network, and top search terms.\"\n\n### 2. Conversion tracking sanity check\n\n```Conversion tracking check\nReview conversion data across all campaigns for the last 30 days. Identify:\n\n1. Campaigns with spend but zero recorded conversions\n2. Campaigns where conversions dropped more than 50% week over week without a matching drop in clicks\n3. Conversion actions that stopped recording entirely during the window\n\nFor each issue, tell me whether the pattern looks like a tracking problem or a genuine performance problem, and explain your reasoning.\n```\n\nBroken conversion tracking is the most expensive silent failure in PPC: Smart Bidding keeps optimizing against bad data while everything looks normal. This separates \"the tag broke on the 14th\" from \"the campaign stopped converting,\" two very different client conversations. Run it weekly. Follow-up: \"show daily conversions for that conversion action over 60 days so I can see when it broke.\"\n\n### 3. Budget pacing check\n\n```Budget pacing\nAsk me for the monthly budget target per campaign before you start (for example: Brand Search 3,000 EUR, Generic Search 8,000 EUR, PMax 5,000 EUR). Use today's date as the day of the month.\n\nPull month-to-date spend per campaign and project end-of-month spend at the current run rate. Flag anything pacing above 110% or below 85% of target, and recommend a specific daily budget adjustment per flagged campaign to land on target.\n```\n\nYou get a pacing table with concrete budget changes, not vague \"consider adjusting\" advice. This prevents the day-28 panic where one campaign quietly spent its month in three weeks. The math is simple; doing it across 15 campaigns and 8 clients by hand is the problem.\n\n## Wasted spend and search terms\n\n### 4. Zero-conversion search terms, clustered into negatives\n\n```Wasted search terms\nPull all search terms from the last 90 days with at least 20 EUR in spend and zero conversions. Cluster them into themes (for example: job seekers, DIY intent, wrong product category, competitor research).\n\nFor each theme, give me total wasted spend, 3 example terms, and a recommended negative keyword list with match types. Order by wasted spend, highe
1st first.\n```\n\nInstead of a raw 400-row search terms report, you get five or six named buckets with a price tag on each. \"You spent 612 EUR on people looking for jobs\" is actionable in a way a wall of queries never is. Sanity-check the proposed negatives, especially phrase match ones that could block good traffic. Follow-up: \"format the top two themes as a paste-ready shared negative list.\"\n\n### 5. Duplicate and overlapping keywords\n\n```Keyword overlap\nAnalyze all enabled keywords across all search campaigns. Identify keywords duplicated or closely overlapping across campaigns or ad groups (same term, near-identical variants, or terms that will match the same queries).\n\nFor each overlap, show which campaign or ad group is winning the traffic, compare their cost per conversion, and recommend which instance to keep and which to pause.\n```\n\nOverlapping keywords mean you compete against yourself, and the keyword serving the query is not always the one with the better ad or landing page. This surfaces those collisions with a keep-or-pause call on each. In accounts that have passed through three managers, expect an uncomfortably long list.\n\n### 6. Low Quality Score keywords burning budget\n\n```Quality Score audit\nFind all keywords with Quality Score 5 or below that spent more than 50 EUR in the last 30 days. For each, break down the three QS components (expected CTR, ad relevance, landing page experience) and identify which one is dragging the score down.\n\nGroup keywords by primary problem and recommend one fix per group: ad copy rewrite, tighter ad group, new landing page, or pause.\n```\n\nA low Quality Score is useless information without knowing which component is the problem: the fix for poor ad relevance (restructure the ad group) is nothing like the fix for poor landing page experience (the client's dev team). This gives you a diagnosis per keyword and a fix per group. Pair with prompt 12 if \"tighter ad group\" keeps coming up.\n\n## Ad copy and creative\n\n### 7. RSA copy from top converting search terms\n\n```RSA copy\nPull the top 20 converting search terms from the last 90 days. Using the actual language people search with, write 15 RSA headlines (max 30 characters) and 4 descriptions (max 90 characters).\n\nBefore writing, ask me for the brand voice, any must-include USPs or offers, and anything to avoid, unless you already have them. Mark which headlines should be pinned and why.\n```\n\nThis grounds your ad copy in the exact phrases that already convert instead of what a copywriter guesses people search for. Check character counts before uploading (Claude is good but not perfect at 30-character math) and make sure claims match the landing page. Follow-up: \"now write a variant of this RSA for a returning-visitor RLSA audience.\"\n\n### 8. Rewrite losers using what the winners share\n\n```Rewrite weak ads\nCompare the top 5 ads by conversion rate against the bottom 5 ads with at least 500 impressions over the last 90 days. Identify what the winners have in common: structure, claims, calls to action, specificity, use of numbers, anything measurable.\n\nThen rewrite each of the bottom 5 ads applying those patterns, keeping each ad's original keyword theme intact.\n```\n\nInstead of generic copywriting advice, you get a pattern analysis derived from what already works in this specific account, then rewrites that apply it. Maybe your winners all lead with a number, or all mention pricing. You will not know until you look, and this prompt looks for you.\n\n### 9. A/B test variants with explicit hypotheses\n\n```A/B test variants\nAsk me for the control ad: paste its headlines and descriptions, or tell me which ad group to pull it from.\n\nGenerate 3 challenger variants for an A/B test. Each variant tests exactly one variable. For each, state: the hypothesis (we believe X because Y), what changes, what stays fixed, and which metric decides the winner (CTR or conversion rate) with a rough minimum sample size.\n```\n\nThe discipline is the point. Most agency \"ad testing\" is launching four random variants and crowning whichever looks best after a week. One variable per variant and a named success metric means the result teaches you something reusable in the next account.\n\n## Bidding and structure\n\n### 10. Bid strategy fit check\n\n```Bid strategy fit\nFor each enabled campaign, list the current bid strategy and conversion volume over the last 30 days. Evaluate whether each campaign has enough conversion data for its strategy: as a rule of thumb, Target CPA and Target ROAS w
1ant 30+ conversions per month.\n\nFlag mismatches (e.g. Target ROAS on a campaign with 8 conversions) and recommend a better-fitting strategy per flagged campaign, including what to expect during the transition.\n```\n\nSmart Bidding on starvation-level conversion volume is one of the most common problems in smaller accounts, and it produces erratic, unexplainable performance. This gives you a one-screen audit of strategy versus data volume. Treat recommendations as a discussion starter: switching bid strategies resets learning, so batch changes deliberately.\n\n### 11. Budget-limited campaigns and reallocation\n\n```Budget reallocation\nIdentify all campaigns and ad groups limited by budget (high lost impression share due to budget). For each, show daily budget, cost per conversion, and search lost IS (budget) for the last 30 days.\n\nThen model a reallocation: moving budget from campaigns with the worst cost per conversion to budget-limited campaigns with the best, keeping total spend flat, what is the projected change in monthly conversions? Show your assumptions.\n```\n\nThis finds the money trapped in mediocre campaigns while your best performers hit their caps by 2pm. The projection assumes marginal performance holds, which it will not perfectly, so treat the number as directional. But \"roughly 12 more conversions per month at the same spend\" is a far stronger client conversation than \"we should shift some budget around.\"\n\n### 12. Structure review: ad groups that are too broad\n\n```Ad group structure\nReview the account structure. For each ad group, compare the keywords it contains against the search terms it actually matched over the last 30 days. Flag ad groups where keywords span multiple distinct intents or product categories, or where matched terms have drifted far from the ad copy.\n\nFor each flagged ad group, propose a split: new ad group names, which keywords move where, and the ad copy theme for each.\n```\n\nBroad ad groups are upstream of half the problems in this list: weak ad relevance, poor Quality Score, generic copy. This finds the ad groups trying to be three things at once and hands you a restructure plan. Prioritize the flagged groups that also carry meaningful spend.\n\n## Client reporting and comms\n\n### 13. Monthly client report in plain language\n\n```Monthly client report\nWrite a monthly performance summary based on actual account data versus the prior month. Ask me for the client name and which month if you don't already have them. Audience: a founder who knows their business but not PPC jargon.\n\nStructure: what happened (3-4 bullets with real numbers), why (plain language, no unexplained acronyms), and what we are doing next month (specific actions, not \"continue optimizing\"). Under 300 words. Honest about what underperformed, no spin.\n```\n\nYou get a client-ready draft that talks about results, not impressions and CTRs the client does not care about. The \"no spin\" instruction matters: clients trust reports that admit what went wrong. Edit before sending, always. Your name is on it, not Claude's.\n\n### 14. Explain a performance drop to a nervous client\n\n```Performance-drop email\nAsk me which client this is and the period to analyze. Pull the data and identify the actual drivers of the conversion drop: seasonality, auction competition, budget changes, tracking issues, landing page changes, anything measurable. Quantify the size of the drop from the data.\n\nThen draft a short, factual, calm email to the client. No defensiveness, no burying the lede. State what happened, what caused it, what is within our control versus not, and the 2-3 things we are doing about it. End with when they will hear from us next.\n```\n\nThe diagnostic half keeps you from sending a reassuring email that turns out to be wrong, and the drafting half keeps the tone steady under pressure. A clear \"here is what happened and here is the plan\" email, sent fast, has saved more retainers than any optimization ever has.\n\n### 15. Quarterly business review summary\n\n```QBR summary\nPrepare a QBR summary covering the quarter versus the prior quarter. Ask me for the client name, the quarter, and the client's stated goal (for example: 40 demo bookings per month at under 150 EUR each) if you don't already have them. Pull spend, conversions, cost per conversion, and revenue, and connect them to that goal.\n\nThen give exactly 3 strategic recommendations for next quarter. Each must name the opportunity, expected impact, what it requires from the client, and what it requires from us. One slide of content per recommendation, maximum.\n```\n\nQBRs are where agencies prove they think about the business, not just the account, and three sharp recommendations beat ten vague ones. The \"what it requires from the client\" line is the quiet power move: it reframes the relationship as a partnership and surfaces blockers that usually get blamed on the agency later.\n\n## How to make these prompts 10x better\n\nEvery prompt above gets dramatically better with one addition: client context.\n\nClaude can see that a campaign converts at 80 EUR. It c
1annot know whether that is great or a disaster unless you tell it the client sells a 49 EUR product, or a 20,000 EUR service with a 30% close rate. Same for margins, seasonality (a pool builder's October \"performance drop\" is called autumn), and what the client considers success.\n\nYou can paste this context at the top of every chat. Or store it once: HYPD keeps a context profile per client (business model, margins, seasonality, goals, brand voice), so every conversation starts informed, whether it is you running the prompt or a teammate covering your accounts. Combined with Claude skills for Google Ads that encode your standard workflows, the prompts stop being one-off questions and become a repeatable system.\n\n## FAQ\n\n### Do these prompts work in the free version of Claude?\n\nThe prompts themselves, yes. The constraint is data access: the free tier cannot connect to your account, so you would paste exports manually, and usage limits make long analyses impractical. For daily agency use you want a paid plan plus a live connection.\n\n### Do I need Claude Code to use these?\n\nNo. These are written for the regular Claude chat interface. Claude Code works too, but nothing here requires it.\n\n### How do I run these on live account data?\n\nTwo options. Manual: export the relevant report from Google Ads and paste it into the chat, which works but gets old fast across multiple clients. Connected: link your ad accounts to Claude through a middleware layer like HYPD, which handles authentication, permissions, and audit logging, so prompts like \"pull all search terms from the last 90 days\" just run. Setup details are in the connection guide linked above.\n\n### Will Claude make changes to my Google Ads account?\n\nOnly if you give it write access, and you should not by default. Every prompt here is read-and-analyze; you stay the one clicking apply. If you want AI-assisted changes later, use a connection with granular permissions so Claude can read everything but write nothing without explicit approval.\n\n### Are these Google Ads AI prompts also usable with ChatGPT?\n\nMostly, yes. The structure is model-agnostic, though data access works differently per tool. The workflows (audit, wasted spend, copy, bidding, reporting) translate directly, as do most AI prompts for PPC: the data connection matters more than the model.\n\n---\n\nWant these prompts running against your live accounts in the next ten minutes? Start a free HYPD trial and connect your first Google Ads account today."])</script>
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1<script>self.__next_f.push([1,"Claude can run ChatGPT ads.\n\nWell, mostly read them, for now. But that's where this is heading.\n\nA couple of weeks ago ChatGPT took their ads platform out of beta and opened it to the public. With it they opened up not just the ads manager but their advertiser API too.\n\nWe're somewhat of a Claude shop at [HYPD](https://www.hypd.ai/), so we couldn't help but think: **can we run them in Claude?** The answer was: not directly. So we used Claude Code to build an MCP Server and bridge that gap. The model context protocol is open source and supported by both Anthropic and OpenAI. The repo is [here](https://github.com/HYPD-AI/openai-ads-mcp) in case anyone wants to jump ahead.\n\nPS: I was having a bit of a deja-vu writing this â a couple of months ago [I was joking about the Claude Ads Agent](https://www.linkedin.com/posts/ionutciobotaru_this-is-a-tough-post-to-write-yesterday-share-7445073743649067009-DIBh/) in Claude Code.. and now⦠here we are :)\n\n## A few notes on ChatGPT Ads\n\nIf you've used Google Ads (or almost any ad manager), the structure is familiar: campaign â ad group â ad hierarchy. The main difference is in the targeting. No demographics, no interests, no audiences; instead, \"context hints\" at the ad-group level: the conversations, topics and keywords where your product might be relevant. In theory they should guide where and when the ads show up. If you've worked with LLMs you'll have a rough intuition for the matching, close to broad match in Google Ads perhaps, though honestly still TBD..\n\nThe rest is early. Geo targeting is mainly the US plus a few English-speaking markets. Reporting is also rather high level; no search-term or context report, so you don't really get to see which conversations triggered your ads.\n\nOne thing I would highlight: there's a change-history log, basically a commit history for your ad account. Useful for humans but for an agent it's *context*: what changed, when, and connecting that with what happened after.\n\n\n*OpenAI Ads Manager â Ad View*\n\nEarly numbers are still settling: CPMs reportedly fell from ~$60 to ~$25 in a few months, CTRs are bouncing around 0.5â2%, and the old minimum is gone. The whole setup \"feels\" less like search and more like discovery â I believe Eric Seufert compared them with Meta Ads rather than Google which I think is directionally correct.\n\nThere are already a few programmatic pipes into the inventory: StackAdapt, Criteo and a few others, as OpenAI goes after Fortune 500s, SMBs and agencies, and plans to expand into apps as well (based on the greyed out conversion pixel options). They are targeting $2.4B ad-revenue for 2026 so let's see which segment will drive that spend.\n\n\n*Mobile app conversion sources*\n\nA fun fact on these ads; It reminds me a lot of the early days mobile in-app native advertising at my previous company:\n\n\n*Native Ads in Tango*\n\nWe ran native ads inside Skype; above the conversations list, icon and copy and description; We also worked with Pinger, TextNow and Tango. Messaging apps, back when the other end of the chat was still a human.\n\nWe had almost no conversation context to target on then; though we did have some demographics we mostly optimised on conversion pixels / postbacks and contextually matching publisher and advertiser apps affinities.\n\nI assume â with no context or inside info â that in the case of ChatGPT we'll see state of the art models doing the optimization; similar to what Meta and Google are using.\n\n## So we built an MCP Server\n\nAnyhow, that's the ads side. The MCP is the more interesting part. Why build it? Mostly because it didn't exist. Nothing on Google or GitHub, which surprised me given how easy OpenAI made the API.\n\nTime to first working version: a couple of hours, give or take; A sign of the times, I guess.\n\n\u003e `I would like to create an MCP for the OpenAI ads API â ` **`Make no mistake`**\n\nLast year building an MCP (AdCP for Google Ads) took me a couple of days and a couple of tries + a rewrite. Claude Code works mostly autonomously by now with a bit of steering pasted in docs, and some slight adjustments..\n\nWhat was easy: OpenAI shipped a clean, straightforward API spec that covers most of what the Ads Manager does. It plays well with agents.\n\nWhat was painful: Claude couldn't easily read OpenAI's own docs, so I ended up copy-pasting them in. (Yes, I could've used a different browser tool, but that's a bit besides the point.) Meaning I couldn't really one-shot it â maybe it could've with a better prompt â but then that's me doing the work upfront.\n\n## What works today; and what doesn't\n\nThe current MVP is read-only and covers the main tools: list and inspect accounts, campaigns, ad groups and ads, and pull performance insights at every level. So you can use Claude to ask how a campaign's pacing, which ad group is eating the budget, or for a summary of last week; and it answers off the live API.\n\n\n*Claude querying the OpenAI Ads API*\n\nWhat it doesn't do yet: write. No creating, editing or pausing campaigns from Claude. That's the roadmap and realistically a couple of prompts away.\n\n*(Note: I am still working through approval and billing to run live spend â one of the reasons I didn't build the \"action\" layer yet + I would rather build it within a deterministic tool call â more on that later.)*\n\n## Next steps\n\nA few things on our list and we'll prioritise them based on customer demand or personal preferences for a
1weekend hackathon:\n\n- Actions (being able to actually manipulate campaigns and)\n- Creating a hosted version (most probably as a connector in HYPD)\n- Bringing a version of this to AdCP (buyer Agent?)\n- Skills for best practices, pixel integration with GTM\n- Future feature updates (ios/android measurement data, email/offline data uploads, maybe different ad units from OpenAI? etc.)\n\nFurther roadmap in the repo: [github.com/HYPD-AI/openai-ads-mcp](https://github.com/HYPD-AI/openai-ads-mcp)\n\nLet us know what you think and if you have any questions either on ChatGPT Ads or the MCP itself.\n\n**References:**\n\n- [Eric Seufert (Twitter)](https://x.com/eric_seufert/status/2042690714585157873) â ChatGPT Ads and Meta/Google\n- OpenAI ads $2.4B 2026 target + programmatic partners â [Sebastian Pinzon (LinkedIn)](https://www.linkedin.com/feed/update/urn:li:activity:7465029451945451521/)\n- Early ChatGPT Ads CTR data â [Reddit r/PPC](https://www.reddit.com/r/PPC/comments/1tdviug/chatgpt_ads_beta_early_ctr_0_115_24/)\n- Anthropic Ads Agent â [LinkedIn](https://www.linkedin.com/posts/ionutciobotaru_this-is-a-tough-post-to-write-yesterday-share-7445073743649067009-DIBh/)"])</script>
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1<script>self.__next_f.push([1,"If you have used Claude for anything analytical, you already know why people want to connect Claude to Google Ads. You can ask plain-language questions about performance, get a campaign audit written in minutes instead of hours, and turn a messy month of data into a client-ready report without touching a pivot table.\n\nThe problem is that most guides answer the wrong question. They tell you *how* to connect it. If you manage your own account, fine. If you manage client accounts, **\"how do I connect it\"** is the wrong first question. The right question is **\"how do I connect it safely\"**, because the moment you wire an LLM into an ad account you are also wiring it into client spend data, customer lists, and (depending on the method) the ability to change live campaigns.\n\nThis guide covers the three ways to connect Claude to Google Ads, what each one actually exposes, and what you need in place before doing this on accounts you do not own.\n\n## The 3 ways to connect Claude to Google Ads\n\nThere are really only three approaches, and each trades convenience against control in a different way.\n\n### Option 1: Export reports as CSV and upload them to Claude\n\nThe zero-setup option. Download a report from Google Ads (campaign performance, search terms, whatever you need), upload the file into a Claude conversation, and ask your questions.\n\n**Pros:**\n\n- No setup, no API access, no developer involvement\n- You control exactly which data Claude sees, because you exported it yourself\n- Claude cannot touch the account, because there is no connection to the account\n- Works today with any Claude plan that supports file uploads\n\n**Cons:**\n\n- The data is stale the moment you export it\n- Every analysis means another manual export, so it does not scale past occasional use\n- Date ranges, segments, and columns are locked at export time, so follow-up questions often need a new file\n- It is surprisingly easy to upload Client A's data into a chat where you were just discussing Client B, and there is no system stopping you\n\nHonest take: **CSV upload is genuinely fine for a one-off analysis on your own account.** If you want a second opinion on last quarter's search terms once a month, you do not need infrastructure for that. The cracks appear when you do this weekly, across multiple clients, with juniors involved.\n\n### Option 2: A raw Google Ads MCP server or direct API connection\n\nMCP (Model Context Protocol) is the open standard that lets Claude talk to external tools and data sources. Several open-source Google Ads MCP servers exist, and a developer can also build a direct connection through the Google Ads API. Either way, Claude gets live access to the account: it can pull fresh data, query any date range, and in many setups also create and edit campaigns.\n\n**Pros:**\n\n- Live data, no exports, no stale numbers\n- Claude can answer follow-up questions by querying again instead of asking you for another file\n- Genuinely powerful: cross-campaign analysis, GAQL queries, automated reporting\n- Open-source options are free apart from your time\n\n**Cons:**\n\n- The connection typically inherits **the full permissions of the Google account you authenticate with**. If your login can edit campaigns, so can the AI.\n- No permission granularity. You cannot tell a raw MCP server \"read-only on this account, no access to that one.\"\n- No audit trail. If something changed in the account, you have no record of whether the AI did it, which prompt triggered it, or who was running the session.\n- Developer setup required: API tokens, OAuth credentials, a developer token from Google, local configuration. Plan for a few hours minimum, more if you have never touched the Google Ads API.\n- On client accounts, this is a real liability. You are giving an experimental, community-maintained tool write access to budgets you are contractually responsible for.\n\nHonest take: a raw Google Ads MCP server is a great fit for a technical solo operator experimenting on their own account. It is a hard sell for an agency, not because the technology is bad, but because it has none of the guardrails client work demands.\n\n### Option 3: A managed safety layer between Claude and Google Ads\n\nThe third approach puts a managed middleware layer between the AI and the ad platform. The AI never holds raw account credentials. Instead, it talks to the middleware, and the middleware enforces rules:
1which accounts are visible, whether access is read-only or write, what gets logged.\n\nThis is the category HYPD sits in. HYPD connects Google Ads (and Meta and TikTok) to Claude and ChatGPT through a permission layer built for people managing multiple client accounts, with client context, built-in PPC expertise, audit trails, and EU hosting.\n\n**Pros:**\n\n- Live data, like a raw MCP connection\n- **Granular permissions** per account: read-only for most work, write access only where you explicitly allow it\n- Client separation, so a conversation scoped to one client cannot pull another client's data\n- A full audit trail of what the AI accessed and did\n- GDPR compliance and EU data residency, which matters if your clients are European\n- No developer setup\n\n**Cons:**\n\n- It is a paid tool, unlike a CSV export or an open-source server\n- You are adding a vendor to your stack, which means vendor due diligence (ask about hosting, data retention, and subprocessors, as you would with any tool touching client data)\n\nHonest take: if you manage one account that you own, you may not need this. If you manage five or more client accounts and want AI in the workflow without betting your client relationships on it, a managed layer is the only one of the three options designed for that situation.\n\n## What you actually expose with raw account access\n\nThis is the part most \"Claude Google Ads integration\" tutorials skip, so let us be specific about what a full-access connection exposes.\n\n**Client spend data.** Budgets, costs, conversion values, revenue figures. For an agency, this is confidential commercial data for every client on the connection, and it now flows through whatever pipeline you built.\n\n**Customer lists and audience data.** Google Ads accounts often contain uploaded customer match lists and remarketing audiences. That is personal data in the GDPR sense. The moment an AI tool can query it, you have a data-processing question to answer, not just a tooling question.\n\n**The ability to change live campaigns.** This is the one that bites. With write access, a prompt like \"clean up the underperforming campaigns\" can be interpreted as an instruction to pause or edit live campaigns. LLMs are good, but they take ambiguous instructions and act on the most plausible interpretation. On a live account, the most plausible interpretation of a sloppy prompt can be an expensive one.\n\n**No record of what the AI did.** With a raw connection, Google Ads change history shows changes made by the connected account, which is your account. There is no log mapping a specific change to a specific AI session and prompt. If a client asks \"why was my top campaign paused on Tuesday,\" you want a better answer than \"we are not sure.\"\n\n## What an agency needs before connecting AI to client accounts\n\nIf you run client accounts, treat this as your pre-flight checklist. Whatever tool or method you choose should give you all five.\n\n1. **Granular permissions.** Read-only should be the default. Write access should be an explicit, per-account decision, not something inherited from your login. Most AI use cases (analysis, audits, reporting) only need read access anyway.\n\n2. **Client-by-client separation.** A workspace or context scoped to Client A should be physically unable to surface Client B's data. Cross-client leakage in a report is the kind of mistake that ends retainers.\n\n3. **Audit trails.** Every query and every action logged, with timestamps, the account touched, and the session that triggered it. This protects you in both directions: it catches problems and it proves your team did nothing wrong.\n\n4. **GDPR compliance and EU data residency.** If your clients or their customers are in the EU, you need to know where the data flows, who processes it, and under what agreement. EU hosting simplifies this conversation considerably.\n\n5. **Client consent language.** Update your contracts or send a short notice before connecting AI tooling to a client account. Something as simple as: \"We use AI-assisted analysis tools to review campaign performance. These tools access reporting data under our supervision, with access controls and logging in place. No changes are made to your account without human approval.\" Most clients respond well to transparency here, and badly to finding out after the fact.\n\n## Claude Google Ads setup with HYPD, step by step\n\nHere is what the setup looks like in practice using HYPD as the safety layer. The whole process takes about ten minutes.\n\n### Step 1: Create your HYPD account\n\nSign up at [hypd.ai](https://app.hypd.ai/login) and create your workspace. If you run an agency, set up your workspace as a team so you can control what each team member can do later.\n\n\n\n### Step 2: Connect Google Ads via OAuth\n\nIn the integrations section, choose Google Ads and authenticate with the Google account that has access to your MCC or individual accounts. This is a standard OAuth flow, so no API tokens or developer credentials are required, and you can revoke access from your Google account at any time.\n\n\n\n\n\n### Step 3: Set the permission level for each account\n\nThis is the step that does not exist in a raw setup. For every connected account, choose the access level. Read-only is the sensible default for client accounts. Reserve write access for accounts where you actively want AI-assisted changes, and even then, on your own accounts first.\n\n\n\n\n### Step 4: Add client context\n\nFor each account, add a short brief: what the business sells, target CPA or ROAS, markets, anything an analyst would need on day one. Claude uses this context automatically, so you stop re-explaining the client at the start of every conversation, and answers come back framed around the right goals.\n\n\n\n### Step 5: Open Claude and start asking questions\n\nConnect HYPD to Claude (the in-app guide walks you through it), open a conversation, and ask about the account. Claude now pulls live data through the permission layer, scoped to exactly what you allowed, with every query logged.\n\n\n\n## The first 5 prompts to run once connected\n\nNot sure where to start? Paste any of these into HYPD and watch it pull your live account data.\n\n```Performance overview\nGive me a performance overview of this account for the last 30 days versus the previous 30. Flag anything that moved more than 20 percent.\n\n```\n\n```Search term cleanup\nReview my search terms from the last 14 days and list queries that look irrelevant, with suggested negative keywords grouped by theme.\n```\n\n```Spend vs. conversions\nWhich campaigns have spent the most this month with the fewest conversions? Rank them and suggest what to investigate first.\n\n```\n\n```Ad copy audit\nAudit my ad copy across active responsive search ads. Where are assets rated low, and what would you test instead?\n```\n\n```Client summary\nDraft a plain-language monthly summary of this account for a client who does not know PPC jargon.\n```\n\n## FAQ\n\n### Can Claude change my campaigns?\n\nOnly if the connection allows it. With CSV uploads, never. With a raw MCP or API connection, usually yes, because the connection inherits your account's full permissions. With a permission layer like HYPD, only on accounts where you have explicitly enabled write access. For most agency work, read-only covers everything you need.\n\n### Do I need to run my own Google Ads MCP server?\n\nNo. Self-hosting an open-source Google Ads MCP server is one option, but it requires developer setup and gives you no permission controls or logging. A managed connection gets you the same live-data capability without running infrastructure or accepting full-access risk.\n\n### Is this GDPR compliant?\n\nIt depends entirely on the method. Exporting customer data into arbitrary AI chats is hard to defend under GDPR. A compliant setup needs a clear data-processing agreement, control over where data is hosted, and logs of what was accessed. HYPD is built for this, with EU hosting and audit trails. Whatever tool you use, confirm these points before connecting client data.\n\n### Does this work with ChatGPT too?\n\nYes. The connection methods in this guide apply to ChatGPT as well, and HYPD supp
1orts both Claude and ChatGPT, so your team can use whichever model they prefer against the same permission layer.\n\n### What does Google allow?\n\nGoogle Ads API access is permitted under Google's API terms, which require proper authorization (OAuth) and responsible use. Reading data and making authorized changes through the API is exactly what the API exists for. What you should avoid is anything resembling scraping or circumventing the API, and any tool that asks for your raw Google password instead of OAuth.\n\n## Try it on one account\n\nIf you want to see what a safe Claude Google Ads integration feels like in practice, start a free HYPD trial, connect a single account read-only, and run the five prompts above."])</script>
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1<script>self.__next_f.push([1,"These last few days I couldnât escape reading about the new [Scope3 Contextual Agent(s)](https://www.adweek.com/programmatic/scope3-ias-doubleverify-ai-agents-brand-safety/).\n\nFrom [Ari Paparo](https://www.linkedin.com/in/aripaparo/) on [Twitter](https://x.com/aripap/status/1900208537264898351?utm_source=news.marketecture.tv\u0026utm_medium=newsletter\u0026utm_campaign=sovrn-s-signal-ttd-s-vibes-and-chrome-spin-out\u0026_bhlid=330f09d343f5a5849f78d47f3d9ec383bec81f8b) to [ADWEEK](https://www.linkedin.com/company/adweek/) and the special report from [ADOTAT](https://www.linkedin.com/company/adotatcom/) (very ð¶ï¸ð¶ï¸ð¶ï¸ - highly recommend).\n\nI have to say, I didnât fully get it at first - but after reading [Brian O'Kelley](https://www.linkedin.com/in/brianokelley/) âs [first post](https://bokonads.com/p/the-end-of-taxonomy) about AI Agents on BOK on Ads I think I got some of it.\n\n(PS: If you know what BOK on Ads is - then I also know weâre probably from the same generation!)\n\nLong story short: a very interesting scope expansion (couldnât resist!) into brand safety \u0026 optimization and overall a daring play across several dimensions both for [Scope3](https://www.linkedin.com/company/scope3data/) and the web at large.\n\nOver the past year weâve had a couple of announcements around GenAI implementations in adtech - the most notorious being [Viant Technology](https://www.linkedin.com/company/viant-technology/) but I also liked [Taboola](https://www.linkedin.com/company/taboola/)'s Abby and the [Vibe.co](https://www.linkedin.com/company/vibedotco/) ones. All quite different, targeting also different customer groups. Though they all broadly focused on advertisers.\n\nThis announcement expands on the previous ones in the sense that it is engaging the other half of the industry - the content side: publishers \u0026 media owners. Chapeau! ð©\n\nComing from the supply side of adtech I can be a tiny bit biased.\n\n## And with my product hat on I appreciate the thought process behind this launch even more:\n\n- Manual process for validation\n\n- Using Claude in manual tests for proving the hypothesis: are LLMs understanding content reasonably well?\n- Layering the Advertiser context on top (from the LLM âmemoryâ; no tools)\n- Lastly, generating the Ad Copy examples based on those 2 datasets.\n\n2. Clear ICP\n\n- Advertisers spending between $10mln and $1bln (I assume this will expand)\n\n3. Clear Value Prop (efficiency gains - cost)\n\n- Estimated cost for running an LLM for each page on the internet is 270k$ on Deepseek, and around 100k in one year (and has been decreasing sharply)\n- At that scale only a few percentages improvement would make the business case green already!\n\n4. Solution!\n\n- Media Agent (hybrid LLM + ML?)\n- Context Agent + Other agents (Audience, Optimization) - which seem to rely on traditional ML signals\n\n## Naturally I do have some comments here:\n- SOTA model cost (Claude) vs open source is quite a different story (even fine tuning those will not make it reasonable in terms of costs at this scale)\n- In terms of zero shot prompt - I think you can go very far with that (but currently costs would be an impediment)\n- The main reason for fine-tuning from my POV (for this specific usecase) relates to costs but I can imagine gaining few points in effectiveness. (note:
1you can fine-tune small models for specific tasks that will rival the SOTA at a fraction of the price)\n- The hybrid approach for LLM and traditional ML makes A LOT of sense for adtech and I belive this will raise the bar - canât wait to see performance reports!\n\n## On top of that there are also some more strategic decisions that also became clear:\n- Leveraging existing infrastructure (connections into DSPs and SSPs, relationships with advertisers and publishers - built via the original business focused on auditing and decarbonizing the adtech supply chain)\n- Building where it makes sense (tech is now ready for this use case: agents)\n- Focusing on a large TAM (brand safety)\n\nNow I think thatâs all fine and good and overall a great move! A move that will position them for what is to come. But what is to come?\n\n## I see this evolution of (digital/programmatic) advertising as two stages:\n- Current industry adopting LLMs and GenAI ð\n\n- Some use it for internal optimizations (incl. customer support, sales, etc.)\n- Others for expanding use cases (example -\u003e Scope3, Viant, etc.)\n\n2. Next wave - reimagining advertising\n\n- Not only on the âopen webâ but also in search (already happening) and social (soon to come).\n- Even the way we interact with devices is changing (especially newer generations) and that will be another tidal shift! ð\n\n\n\n- With that said (and promise Iâll end with this) I would love to see the [IAB](https://www.linkedin.com/company/iab/) - specifically the [IAB Tech Lab](https://www.linkedin.com/company/iabtechlab/) with [Anthony Katsur](https://www.linkedin.com/in/akatsur/) take the ball from here and run with it. Specifically in terms of agents interoperability but also exposing web content to LLMs without the ânoiseâ.\n- We already have [LLM.txt](https://github.com/AnswerDotAI/llms-txt) and [MCPs](https://github.com/cline/mcp-marketplace) are starting to pop-up. Plus most of the adtech infrastructure has been based on open standards and open source tech ([Prebid.org](https://www.linkedin.com/company/prebid-org/) FTW!). We took some detours with mobile (apps) but it seems weâre getting back on track with GenAI and itâs time to build!\n- Letâs go! ðð¤âð\n"])</script>
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1<script>self.__next_f.push([1,"Itâs been two years since we put together [our first GenAI landscape ](https://www.linkedin.com/feed/update/urn:li:activity:7135700718711181314/)together with Hugo. Back then we manually curated the list (in Excel), the assets (logos, descriptions, etc.) and had a freelancer build the deck/PDF.\n\nAt that time, many marketing companies were just starting their journey into GenAI and many others were just starting altogether. A lot has changed since, and arguably GenAI has become mainstream in marketing both in terms of diversity of tools but to a certain extent in terms of adoption as well.\n\nFor example this GenAI Marketing Landscape was built with several tools:\n\n- vibe coded the MVP with Replit (great at landing pages. or any other type of web app)\n- deep researched with Perplexity and Strawberry Browser\n- and we made tool updates and changes with Claude Code\n\nNot only that, but since then we went from writing about tools to actually building a few together withPhilipandSimon. Some of them are live, some will be live in due time and some are iterations on previous versions (while some weâve shut down).\n\nMore recently we saw two similar works being published. First, the AI Lumascape byLuma Partners(the OGs so to say) and the Knowledgescape fromU of Digital.\u2028The first one focuses on a birdâs eye view on AI impact on marketing while the other on education and a deeper level of categorization. And the first one got quite a bit of feedback fromBrian OâKelleyrefocusing the framing from tech components to customers, workflows and what is / and will become agentic in the future.\n\nHereâs our attempt to take into account some of that feedback (mostly via use-cases where we added: ICP \u0026 Segmentation, AI Commerce and AI-Native Media) while focusing on present day practicality and evolving practices - thus including open source tools and MCPs as well as startups, indie apps and even specific GenAI products that larger companies might have.\n\n\n\n## Who is it for\nOur goal is to offer a curated list of tools for marketing practitioners - leveling the playing field (at least in this landscape) between startups, large tech companies and even open source tools. It should ideally be faster than asking a chatbot, easier to use and hopefully a good snapshot of the current GenAI marketing landscape (2025, December) - without discounting the upcoming shifts: agentic commerce and GenAI-as-medium.\n\n## How to use it\n\nWe complement the categories from the previous landscape(s) with the following break-down:\n\n- Plan - includes Audience intelligence and Competitive Analysis with products such as kAI from Kayzen and Samba TVâs MCP - including yours truly - HYPD AI\n- Create - including companies like Hera and Streamr or Arcads and Poolday.ai\n- Activate - where we include the buy side with Viant AI or the buyer agent from Scope3 (and where we included the AdCP protocol as well) as well as the Agentic Commerce Protocol\n- Measure - including Singularâs MCP and for example Googleâs Analytics Advisor\n- General - itâs where we include multi use tools such as N8N or Zapier.\n\nPlus we made the whole landscape easily searchable with a directory view (searches across description, categories, use cases, etc.) and a couple of filters.\n\n\n\nIn terms of use cases we have: Research, Audience discovery, Copywriting, Ad Creative, Video generation, Content, Campaign automation and quite a couple more (feedback here would be welcome though!)\n\nWe recognize we missed many names so if some obvious ones come to mind donât hesitate to use the submission form on the website.\n\n## What we excluded\nLastly we thought it shouldnât overlap too much with previous work so we excluded a couple of categories of products or companies:\n\n- traditional ML companies\n- large general tools (ChatGPT, Claude) - though we might include Apps and MCPs within those platforms\n- we selected only some OSS MCPs/Tools (either the official ones, or the ones more frequently maintained)\n- other infra companies: DMPs/DSPs/SSPs that donât (yet) have a GenAI product/offering on the market\n\n## (possible) Next steps:\n- this is a work in progress - so weâre continuing polishing it almost daily\n- we plan to make the data available as MCP and/or as a ChatGPT App?\n- and add popularity or votes (analytics already installed!)\n- as well as add a comparison feature\n- and channel/format breakdowns\n- while automating the submission/addition of tools (browsing, scraping, structured outputs)\n\nWithout further ado, here is the link: [https://tools.hypd.ai/](https://tools.hypd.ai/)\n\n[](https://tools.hypd.ai/)PS: there are some easter eggs in there - will let you find them.\n\nThis article was originally posted on[ Medium](https://weeb0.medium.com/genai-x-marketing-landscape-2025-edition-2b8617e89f85)\n"])</script>
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1<script>self.__next_f.push([1,"## I have a problem.\n\nI see a seed of an idea and I want to share it â then I think about it more and as I write it, it cascades into other ideas â and then I have a bit of a hard time putting them back together again.\n\nThis is one of those times.\n\nAnd this time itâs about AdCP vs oRTB (and the broader agentic advertising discourse).\n\nIâve seen many comments and interpretations around that. The definition that stuck is the âoldâ one:\n\n\u003e âAdCP is budget allocation / investment, while RTB is day trading.â\n\nBut the more interesting artifact (to me) is the thesis behind it:\n\n\u003e âExecution costs limit how many media partners advertisers can work with. The opportunity isn't optimizing existing platforms better â it's expanding to more partners without scaling headcount. AI agents collapse this complexity cost. This is an allocation problem, not an efficiency problem.â\n\nI agree with that. And I also want to add something to it. I think there are in fact two problems:\n\n- One is indeed allocation (highly recommend [Eric Seufert](https://www.linkedin.com/in/ericseufert/)'s post on [digital advertising optimally routing demand](https://mobiledevmemo.com/does-advertising-create-demand/))\n- The other is delivery (performance performance inside the platform: how well the platform turns attention into advertiser outcomes; and the reason we have Applovinâ in S\u0026P 500 / Nasdaq 100)\n\nSo yes, allocation is important. But the larger unlock (in my head) is allocation on top of good delivery.\n\nI found the ânew framingâ from the [pull request](https://github.com/adcontextprotocol/adcp/pull/718/files#diff-1f65cfc6393893b230d8b818f179d5e559a0deaf5ba87282d92c154558cb1eb6) (thanks [Brian O'Kelley](https://www.linkedin.com/in/brianokelley/) for #buildinginpublic) highly relatable. It also captures the market structure pretty well:\n\n\u003e â90% of ad spend never touches RTB â it lives in large platforms, direct deals, and premium inventory.â\n\nThat explains why allocation is hard in practice. We talk about âGoogle vs Meta vs Display vs TV/CTVâ⦠but there are so many channels (platforms/publishers) out there, and they do need to be operated rather independently while iterating across them is still painful.\n\nThe other side of the story is scale.\n\nWhy is Meta so good? Itâs not just that you can allocate. Itâs that it has scale â and it delivers. You might find smaller channels that perform, but there are too many of them and even in aggregate you wonât get the same delivery as Meta for example.\n\nThatâs why I think the first place agentic advertising will have an impact is allocation across larger platforms and secondarily across smaller ones.\n\nAnd while doing that it also equalizes a bit the playing field between larger and smaller brands:\n\n- Big brands get better allocation\n- Smaller players get access to many more inventory sources / publishers / networks that they couldnât operationalize before\n\nThese advantages are still not equal across segments, obviously. Small budgets + learning signals are real constraints. So it takes time. But directionally, it makes the market more liquid.\n\nOne more angle (I donât have KPIs for this but it should be easy to check): large platforms also tend to be more efficient across the funnel (impression -\u003e engagement -\u003e LTV). This allows them to generate more value per impression/attention unit (their ARPU vs other ad supported businesses), meaning advertisers can often get the same outcome with less waste â so cost per outcome stays lower (vs most RTB channels). And with that they get larger budgets â more data â more effective transactions â virtuous cycle.\n\nThat advantage wonât be easy to match. And itâs an advantage of delivery. Carefully built over years (decades) and thoroughly scaled and optimized.\n\nThatâs half the story though. Platforms have their own interests (profitability, growth, etc), sometimes at odds with advertisers (and even users - eg âdoom scrollingâ). Which means there is o
1pportunity for re-allocation and value recapture.\n\nAnd in that case the operating model would change.\n\nYes the platforms get more automated but I havenât heard that there is less work to do. Different for sure, but rarely less. And that work can change from pure operational to managing multiple agents, reviewing their âpull requestsâ (or the equivalent in advertising) rather than pushing buttons.\n\ntl;dr the wedge I see has two sides: allocation + delivery; leveraging headcount + agents to recapture market power/control and unlock value.\n"])</script>
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1<script>self.__next_f.push([1,"## Intro\nI read [Principles of building AI Agents ](https://mastra.ai/book)a couple of months back, but only got the paper copy few weeks ago. Iâve been carrying it around quite a bit â mostly to get a better picture of it as others have already shared theirs.\n\nWhat was interesting to me was using primitives as the foundation of building agents. When we started building agents ( early 2024) there was very little material around it. With IndieGrow we even built a âdeep researchâ tool before it was âcoolâ or there was much literature around it though now almost every lab an middleware company has a variation of a deep agent.\n\nAnyhow, I highly recommend the read; I was originally planning to share just my reading notes but realized I had a few pages of highlights, so I did a proper write up including our experiences so far.\n\nThe book doesnât have a table of contents but I figured it would be useful to share one:\n\n- Part I â Prompts\n- Part II â Building Agents\n- Part III â Workflows\n- Part IV â RAG\n- Part V â Multi-Agent System\n- Part VI â Evals\n- Part VII â Dev \u0026 Deployment\n\nAnd I added also:\n\n- a short Outro section with some of the emerging primitives/best practices weâve encountered\n- a couple of other bookmarks I found relevant (Further Reading)\n\n## Chapter I â Prompts (and Context Engineering)\n\n\u003e Zero-shot: The âYOLOâ approach. Ask the question and hope for the best. Single-shot: Ask a question, then provide one example (w/ input + output) to guide the model Few-shot: Give multiple examples for more precise control over the output.We actually built a prompt CMS into Mastraâs local development environment for this reason.CAPITALIZATION can add weight to certain words. XML-like structure can help models follow instructions more precisely. Claude \u0026 GPT-4 respond better to structured prompts (e.g., task, context, constraints).\n\n#### Notes\nWe have seen this first-hand. When we started at HYPD, we were testing the models with âofflineâ datasets against both ChatGPT and Claude â trying to see how far we could push pure LLM performance before adding prompts. We realized early on that prompt versioning and structure werethe key to getting the desired output. Itâs the most fundamental primitive, and honestly, I didnât understand how deep it goes until I listened to [Andrej Karpathy on the Dwarkesh podcast](https://www.dwarkesh.com/p/andrej-karpathy)*.\n\nEven now, a lot of our time is spent breaking prompts apart â looking at structure, words, testing small differences, and seeing how they affect outputs.\n\n\nContext Engineering components\n\nMore interestingly now this âdisciplineâ evolved into[Context Engineering](https://x.com/Saboo_Shubham_/status/1984809452487508027)as it includes more individual components like tool descriptions, examples, message history, memory, RAG and other pieces of content and context that are made available to the model.\n\nIn practice, because LLMs are âstatelessâ you either build the context around the agent or use various frameworks (like Mastra or Langgraph) to help handle this.\n\n## Chapter II â Building an Agent\n\n\u003e Most models support âstructured outputâ to enable this.Use semantic naming that matches the toolâs function (eg multiplyNumbers instead of doStuff)semanticRecall indicates that weâll be using RAG (more later) to search through past conversations. topK is the number of messages to retrieve. messageRange is the range on each side of the match to include.\n\n#### Notes\n\nIn terms of structured outputs it was an interesting development (returns âdeterministicâ json or similar format). We do use that across a couple of our agents â from the intent classifier to the SQL generator or for error message generation. OpenAI pioneered it and made it quite reliable. I think this is even more useful for agent âactionsâ.\n\nBefore tool use, thereâs a question about how we define agents and the current [mainstream definition](https://simonwillison.net/2025/Sep/18/agents/)* is:\n\n\u003e an LLM plus tool(s) with a goal, running in a loop to complete that task\n\nWe use different tools like SQL/GAQL generation, or rendering charts. Originally the tools were paired
1individually with the agents and we trace when they are used.\n\nIn terms of semantic naming â I think this one speaks for itself. I donât know if we need to use it, but in general semantic names are easier for models, easier for humans to read or for [DeepWiki](https://deepwiki.com/adcontextprotocol/adcp)* to connect the dots.\n\nIn terms of RAG (Retreival Augmented Generation), this is a slightly more complex topic and is covered a bit later. High level â it fits into the broader[Context Engineering](https://www.llamaindex.ai/blog/context-engineering-what-it-is-and-techniques-to-consider)primitive with all its components. You can do RAG across many of the steps. But for example for messages and message history â we provide full context. While for memory/personalization it might make sense to not load everything in the memory but just the relevant parts.\n\n## Chapter III â Graph-Based Workflows\n\n\u003e You could have one LLM call check for 12 symptoms. But thatâs a lot to ask. Better to have 12 parallel LLM calls, each checking for one symptom.Sometimes, youâll want to fetch data from a remote source before you feed it into an LLM, or feed the results of one LLM call into another. In Mastra, you chain with the .then() command.Itâs helpful to compose steps in such a way that the input/output at each step is meaningful in some way, since youâll be able to see it in your tracingworkflows need to pause execution while waiting for a third-party (like a human-in-the-loop) to provide input.So to design a good user experience for your agent, make sure your workflows and agents stream intermediate step completion (Mastra has .watch() for this), then present these to the user in a way that maximizes snappines\n\n\nAgentic workflows examples\n\n#### Notes\nBack in the âearlyâ days we separated the agents in: LLM + tool call + prompt. Keeping them very specific, which was closer to workflows primitive, on which we built for quite a while.\n\nThese days, as SOTA models improved across several dimensions(tool calling, context recall, negative instruction following, etc.)we give the agent more autonomy and more tools, and it can choose which tools to use at which time and deliver the answer. Thatâs one of our more recent learnings.\n\nNext learning is about chaining. Sometimes you want to fetch data from a remote source before you feed it into the LLM. I think this is extremely relevant in our case as we have a lot of external data, either from SQL or GaQL or GraphQL. We have multiple tool calls that need to query the data, either through an API or SQL syntax and return the results. Thatâs another important piece of the workflow. We almost always have a data retrieval step.\n\nIn terms of composing these steps, we have highly specific knowledge related to advertising where a lot of metadata needs to be added into the context window in order for the query to be correct. For example when you run a natural language prompt to query you need the relevant data for the model to âunderstandâ the question. By default â and using just model knowledge the results will be either wrong or useless. Sometimes you also need to âaugmentâ the answer after retrieval where semantic data is added back into the pure tabular dataset we got back in the previous step.\n\nGiven all these steps the whole âagentic flowâ can take even a couple of minutes. Users on the other hand want to see progress, not wait in silence. So we added partial responses or visible âthinkingâ steps which improved trust and usability.\n\nLastly â in terms of human in the loop, we donât yet perform âreal worldâ actions (against campaigns for example) but we can, for example, ask questions from the human to better specify the query, guide/approve for next actions, or just to clarify certain parts of the workflow/plan. And thatâs another important part of how we build agents. There is a thin line though between given the agent autonomy (run with best guess) and hand-holding (asking questions at each step)\n\n## Chapter IV â RAG\n\n\u003e The implementation is similar to a geospatial query searching latitude/longitude, except the search goes over 1536 dimensions instead of two.Reranking: Optionally, after querying, you can use a reranker. Reranking is a more computationally expensive way of searching the dataset.\\In practice, the most important thing is to prevent infra sprawl (yet another service to maintain).If youâre already using Postgres for your app backend, pgvector is a great choice. If youâre spinning up a new project, Pinecone is a default choice with a nice UI. If your cloud provider has a managed vector DB service, use that.Upsert operations allow you to insert or update vectors and their associated metadata in your vector store.Hybrid Queries with Metadata. Hybrid queries combine vector similarity search with traditional metadata filtering. This allows you to narrow down results based on both semantic similarity and structured metadata fields like dates, c
1ategories, or custom attributes.(use) graph databases to model complex relationships.\n\n#### Notes\n\nWeâve learned some lessons here also â around keywords, and similarity search vs relevancy and reranking. Weâre light users of these techniques â but if you have a large textual/hybrid dataset: think tens of thousands of rows with multiple columns â it comes in handy to know what is possible and for which use cases.\n\nIn general semantic recall and RAG is a rather complex topic. In Berlin we found that there are both middleware (Needle) or infra (Weaviate) vendors that usually are eager to help; Easy to meet at meetups or other communities. For advertising specifically we looked at natural language queries (ânaive RAG) and agentic RAG (multiple loops, query rewriting) as well as reranking (for relevancy), or a combination of these.\n\nWeâve of course first tried to âfillâ the context; But you easily get to [context rot](https://research.trychroma.com/context-rot)* or ânoisyâ context â âconfusingâ the LLM. Most generic / horizontal chatbots rely on the larger context windows but with vertical agents you can actually put checks and balances in place and make sure you get the desired output.\u2028Our tooling here stands from Qdrant for VectorDB, Cohere for reranking, OpenAI for embeddings but weâve also tried off the shelf tools like Needle or Llamaindex.\n\nOne newer development to note is also [Skills* in Claude ](https://simonwillison.net/2025/Oct/10/claude-skills/)which seems a promising tool in the toolbox (more on that at some other point).\n\n## Chapter V â Multi-Agent System\n\n\u003e different system prompts, and access to different tools.Each of these workflows can then be passed along as tools to the agent(s).(And in fact the code writing agent is two different agents, a reviewer and writer that work together.)These are inverse examples to one another, which brings us, again, to an important takeaway: all the primitives can be rearranged in the way you want, custom to the control flow you want.\n\n#### Notes\n\nAs I mentioned previously we now give the agent more autonomy and more tools, and it can choose which tools to use at which time in order to deliver the task. This has been mainly enabled by reasoning models and itâs one of our more recent learnings; Think of it as Claude Code vs n8n workflows. We did use chain of thought before (âthink of the solution step by stepâ) â it worked to a certain extent but we see much better results with the latest SOTA models â which now also leverage subagents* (specialized agents, with limited context/tools â focused on subtasks)\n\nTheinterface interchangeability is yet another interesting primitive in regards to agents.For example HYPD can be made available as an API and wrapped in as an MCP (Model Context Protocol). We actually used quite few MCPs under the hood for testing (for GAQL, GraphQL and quite a few others).\n\nThe way we think about it currently is that itâs fluid and it should be made available where the user workflow happens (so if it needs to be in in the Intercom chatbot or available in n8n for different workflows â or even orchestrating ourselves other MCPs â dependent on customer needs, long term plans and so on. Different providers already answer this question differently. But we believe in inter-operability; The A2A standard for Google fits this specific purpose and the AdCP (Ad Context Protocol) supports both.\n\n## Chapter VI â Evals\n\n\u003e Prompt engineering evals explore how different instructions, formats, or phrasings of user queries impact an agentâs performance. They look at both the sensitivity of the agent to prompt variations (whether small changes produce large differences in results) and the robustness to adversarial or ambiguous inputs.Many correctness aspects (e.g., subtle domain knowledge, or an unusual user request) canât be fully captured by rigid assertions, but human eyes catch these nuances.\n\n#### Notes\n\nOne of the most important dataset is translating your customersâ needs expressed into natural language (if that is the input) â but also what the desired outputs of those needs are so you can test against!.\n\nIn our case weâre building conversational agents â itâs basically queries and answers â but as we focus on structured data â data accuracy is paramount so we need intermediate steps evals (for tool calling or individual agents) â which for example most vendors donât offer out of the box (Langfuse offers it as code, but not in the UI)\n\nOnce again â the easiest way is to start manually (questions/answers) and then automate as scale and number of use cases grow. Once in production and at scale we are now using actual user queries â both for good and bad examples as evals.\n\n## Chapter VII â Development \u0026 Deployment\n\n\u003e Agent Chat Interface: Test conversations with your agents in the browser, seeing how they respond to different inputs and use their configured tools. Workflow Visualizer: Seeing step-by step workflow execution and being able to suspend/resume/replay Agent/workflow endpoints: Being able to curl agents and workflows on localhost (this al
1so enables using eg Postman)Tool Playground: Testing any tools and being able to verify inputs / outputs without needing to invoke them through an agent. Tracing \u0026 Evals: See inputs and outputs of each step of agent and workflow execution, as well as eval metrics as you iterate on code.But from a developer perspective: the ideal runtime for agents and workflows is serverless, and autoscales based on demand, with state maintained across agent invocations by storage built into the platform.Thereâs a large number of observability vendors, both older backend and AI-specific ones, but the UI patterns converge:\n\n\nTracing UI example\n\n#### Notes\nIn general software development best practices are not that different from building agents; Youâll need a local environment; You might also want a staging environment to QA things properly.\n\nWhat is different is that indeed we are coding with agents (Claude Code, Amp, Devin) and using MCPs to accelerate learnings, development, fix bugs or review code.\n\nIn terms of tooling we found that testing tools (MCPs) with Claude before development helps us understand the edges better and spec the tickets accordingly.\n\nAnother testing platform weâve used extensively is n8n â you can get A LOT done before getting to development.\u2028Other than that â having proper access to the database (for message history, tool calls) is sometimes helpful when traces are not enough.\u2028Some of these features (message history, tool calling, and even evals) â can be abstracted away behind for example Responses API from OpenAI or Skills behind Claude API / Agents SDK. We havenât picked a side yet :) â and for now weâd like to keep the optionality for OSS models also.\n\nLast note here â we refactored the platform a couple of times so far and every time we were both faster and having a more robust product at the end of it. Now we have made some bets which hopefully will keep us on this trajectory for at least a while (a year+ or more!)\n\n#### Outro\n\nThere are a couple more points that weâve either stumbled upon in our practice or we think will be relevant in the future or maybe even in the present for other builders.\n\nFor example we see a lot of âhorizontalâ agents with tens of standard integrations (from Gmail, to Dropbox, to MSuite and so on) but only a few that have vertical specific integrations (for Marketing/Advertising for example).\n\nAnd because these use cases are not yet mainstream some of the solutions (see Google auth for MCPs) might still use âtraditionalâ connectors rather than agentic ones.\n\nThe other piece is hype vs reality. We did talk about MVPs, and POCs vs production. And the gap is the biggest there. On one side we see large projects deployed on [Langgraph ](https://www.langchain.com/customers)(Klarna, Vodafone, etc.) but at the same time we also see a flood of many open source components / repos â with thousands of weekly downloads â that are often not updated for months and will probably get abandoned?\n\nSometimes a simple workflow in a no-code tool is enough; And building an agent needs to be rooted in real business use cases â and that applies for open source repos as well. There are subtle differences between agents and workflows and these days they can work in concert.\n\n\n\nOutside of the hype, the industry does move at an accelerated pace. We went from prompt engineering to context engineering and from âmanualâ tool use and workflows to MCPs, skills and deep agents. More primitives and best practices are following across each of these as more companies take AI agents to production ([especially lessons from Claude Code ](https://www.anthropic.com/engineering/building-effective-agents)team!*). We have now long running agents, agents built via chat, ambient / proactive agents plus already advanced methods for context engineering (like DSPy, handoffs, etc.) and tool use (Code Mode). As these are relatively new developments weâll share what we learn as we experiment with bringing the relevant ones into our production agents.\n\nLastly I think itâs helpful to identify the components of the stack â and which parts youneed to build, customize or buy; At the high level there are overlaps between thefoundation layer, middleware and application layer, but as you go deeper, the demarcation lines become clearer (in terms of actual value add and solving specific vs generic problems). Going through this process also helps narrow your scope as well also picking the right tool based on the companies focus/direction.\n\n#### Further Reading\n\n- [Principles of Building AI Agents](https://mastra.ai/book)\n- [Context Engineering](https://x.com/Saboo_Shubham_/status/1984809452487508027)\n- [Dwarkesh Patel / Andrej Karpathy](https://www.dwarkesh.c
1om/p/andrej-karpathy)\n- [Simon WIllison â Agents Definition](https://simonwillison.net/2025/Sep/18/agents/)\n- [DeepWiki](https://deepwiki.com/adcontextprotocol/adcp)\n- [Zapier â AI Automation Spectrum](https://x.com/petergyang/status/1982815634280120493)\n- [Skills and SubAgents](https://x.com/omarsar0/status/1981798842866557281?s=46\u0026t=TQKf9kmbapslyfR2AwNQWg)\n- [WTF is an agent](https://www.ashpreetbedi.com/articles/wtf-is-an-agent)\n- [Llama â Context Engineering](https://www.llamaindex.ai/blog/context-engineering-what-it-is-and-techniques-to-consider)\n- [Claude â building agents](https://www.anthropic.com/engineering/building-effective-agents)\n- [Manus â Context Engineering](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus)\n"])</script>
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