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https://mirawealth.io/assets/planning-agents-with-receipts-CE7xxVP_.js

js mirawealth.io collected 2026-10-09 22:53:50 UTC 4,960 bytes, 1 lines download raw bytes

1import{j as e}from"./index-8vE9Fqt6.js";function s(){return e.jsxs(e.Fragment,{children:[e.jsxs("section",{className:"mb-10 rounded-xl bg-gradient-to-br from-blue-50 to-white border border-blue-200 p-6 shadow-sm",children:[e.jsx("h2",{className:"text-xl font-semibold text-slate-900 mb-3",children:"Summary"}),e.jsxs("ul",{className:"list-disc list-inside text-slate-700 space-y-2",children:[e.jsx("li",{children:"Agentic AI means workflows, not one-off prompts: gather, simulate, compare, recommend, document, follow-up."}),e.jsx("li",{children:"The non-negotiables for advisory firms: reproducibility, explainability, and auditability. Every result must be rerunnable and exportable."}),e.jsx("li",{children:"Mirawealth’s edge: tax-aware agents (BETR/IRMAA), estate overlays that reconcile to retirement principal, deterministic Monte Carlo, and demo sandboxes that protect production data."})]})]}),e.jsxs("section",{className:"space-y-6 text-slate-700 leading-relaxed",children:[e.jsx("h2",{className:"text-2xl font-bold text-slate-900",children:"From chatbots to planning agents"}),e.jsx("p",{children:"The first wave of AI answered questions or summarized PDFs. Helpful, but decisions in wealth management demand more than a clever paragraph, they demand workflows and receipts. Agentic systems encode the planning loop end-to-end and hand you two things: recommended actions and proof of how you got there."}),e.jsx("h2",{className:"text-2xl font-bold text-slate-900",children:"The three R’s: Reproducible, Reviewable, Regulator-ready"}),e.jsxs("ul",{className:"list-disc list-inside text-slate-700 space-y-2",children:[e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Reproducible"}),": Same inputs, same outputs. Use seeded simulations, versioned assumptions, and frozen scenarios to permit re-runs months later."]}),e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Reviewable"}),": Every suggestion ships with the “why”: key drivers, toggles, alternatives considered, and trade-offs."]}),e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Regulator-ready"}),": Exportable artifacts (time stamps, model versions, disclosures, and client acceptance) align with recordkeeping and marketing obligations."]})]}),e.jsx("h2",{className:"text-2xl font-bold text-slate-900",children:"Agent skills advisors actually need"}),e.jsxs("ul",{className:"list-disc list-inside text-slate-700 space-y-2",children:[e.jsx("li",{children:"Data hygiene with consistent keys across CRM, accounts, goals, tax items, and estate topics so joins are reliable."}),e.jsx("li",{children:"Simulation literacy: deterministic Monte Carlo with calibrated tail risk; tax engines that model brackets, credits, IRMAA, NIIT, RMD/QCD choreography."}),e.jsx("li",{children:"Decision diffs that reconcile principal: “with vs without” analyses that attribute deltas to composition vs. bounded wealth-effects, not hidden principal inflation."}),e.jsx("li",{children:"Documentation by design: client summary and internal memo generated in the same pass."})]}),e.jsx("h2",{className:"text-2xl font-bold text-slate-900",children:"Mirawealth’s take: planning agents with receipts"}),e.jsxs("ul",{className:"list-disc list-inside text-slate-700 space-y-2",children:[e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Tax-aware strategy center with BETR logic"}),": break-even tax rate guidance, lifetime tax savings, and IRMAA awareness to time conversions intelligently."]}),e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Estate overlay that reconciles to retirement"}),": constant-sum estate deltas keep principal consistent and explain composition vs wealth effects."]}),e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Deterministic Monte Carlo"}),": seeded RNG for reproducible outputs and trusted comparisons across scenarios."]}),e.jsxs("li",{children:[e.jsx("span",{className:"font-semibold text-slate-900",children:"Demo sandbox guard"}),": advisors can demo without touching production data; requests are tagged and persistence is disabled."]})]}),e.jsx("h2",{className:"text-2xl font-bold text-slate-900",children:"Where to start"}),e.jsxs("ol",{className:"list-decimal list-inside text-slate-700 space-y-2",children:[e.jsx("li",{children:"Define your canonical artifacts and templates (client-facing and internal memo)."}),e.jsx("li",{children:"Standardize IDs across systems so data joins are reliable."}),e.jsx("li",{children:"Version assumptions and seed simulations for reruns; export full “receipts.”"}),e.jsx("li",{children:"Embed compliance rules into generation: disclosures, suitability, and retention."}),e.jsx("li",{children:"Route exceptions to a QA queue; let the rest flow automatically."})]})]})]})}export{s as default};

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