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https://moai.team/contacts

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1<meta name="next-size-adjust" content=""/><title>Contact | Moai Team</title><meta name="description" content="Need help with your software development project? Moai Team helps business owners achieve their goals. We build partnerships, not just code. Learn more."/><meta name="application/ld+json" content="{&quot;@context&quot;:&quot;https://schema.org&quot;,&quot;@type&quot;:&quot;WebSite&quot;,&quot;name&quot;:&quot;moaiteam&quot;,&quot;url&quot;:&quot;https://moaiteam.com&quot;}"/><link rel="icon" href="/en/icon0.svg?e81a825f0a605638" type="image/svg+xml" sizes="any"/><link rel="icon" href="/en/icon1.png?1f1d6c6febfc4717" type="image/png" sizes="96x96"/><link rel="apple-touch-icon" href="/en/apple-icon.png?6d937111d6fa050f" type="image/png" sizes="180x180"/>
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264image\":\"/images/chatbot-cycle-3.png\"}],\"lifeCycle_title\":\"Chatbot development process\",\"lifeCycle_description\":\"Our chatbot development process is designed to ensure that your chatbot is a success. We're here to guide you through every step of the way, from concept to delivery.\",\"faq\":[{\"title\":\"What is a chatbot?\",\"description\":\"A chatbot is a computer program that uses artificial intelligence to simulate the conversation or \\\"chat\\\" with the user.\"},{\"title\":\"What is chatbot development?\",\"description\":\"Chatbot development is the process of building a chatbot, or an automated conversation system. This can be accomplished through a combination of artificial intelligence, natural language processing, and machine learning.\"},{\"title\":\"What are the two types of chatbots?\",\"description\":\"There are two main types of chatbots: rule-based and machine learning. Rule-based chatbots use rules written by humans, while machine learning chatbots learn from interactions with users and improve over time.\"},{\"title\":\"How long does chatbot development take?\",\"description\":\"A simple chatbot can be built in as little as 1 month, while one with more complex functionality may take up to 3 months.\"},{\"title\":\"How much does chatbot development cost?\",\"description\":\"The cost of chatbot development can range from $1,000 to $20,000. The difference in price is largely due to the complexity and sophistication of the bot's functionality.\"},{\"title\":\"What platforms can I develop my chatbot for?\",\"description\":\"You can build your chatbot for Facebook Messenger, Slack, Skype and other platforms. We will work with you to determine which platform is best for your needs.\"},{\"title\":\"What are the advantages of using chatbots?\",\"description\":\"Chatbots have many advantages over other forms of customer service. They're faster, more efficient and allow you to reach more people at once. They also reduce costs by eliminating expensive call centers and live chat operators. Chatbots are especially useful when you want to provide 24/7 support.\"},{\"title\":\"Why should I outsource chatbot development?\",\"description\":\"Outsourcing chatbot development allows companies to save time and money while also making sure your chatbot is built using best practices for creating great conversational experiences.\"},{\"title\":\"Why should I choose Moai Team for my chatbot development project?\",\"description\":\"Our experts can create a chatbot that will be able to interact with your customers in a personalized way, helping you improve the customer experience, increase sales and reduce costs.\"}],\"getInTouch_description\":\"We’d be happy to answer any questions you may have\"},\"contacts\":{\"pageTitle\":\"Get in touch\",\"pageDescription\":\"Do you need help with your software development project? We’re delighted to offer a free, no-obligation consultation to answer all your questions and give honest advice.\",\"breadcrumb\":\"Contacts\",\"form\":{\"thankYouTitle\":\"Thank you for reaching out!\",\"thankYouText\":\"We will examine your message and get back to you with an answer as soon as possible.\",\"backToHomepage\":\"Back to Homepage\",\"formTitle\":\"Send us a message\",\"formDescription\":\"We help business owners with everything from planning and strategy to implementation and ongoing support.\",\"nameLabel\":\"Name\",\"namePlaceholder\":\"Write your name\",\"emailLabel\":\"Email\",\"emailPlaceholder\":\"Write your email\",\"phoneLabel\":\"Phone\",\"phonePlaceholder\":\"Write your phone\",\"messageLabel\":\"Message\",\"messagePlaceholder\":\"Please let us know if you have any questions\",\"privacyPolicyText\":\"I accept your\",\"privacyPolicyLink\":\"Privacy Policy\",\"send\":\"Send\",\"validation\":{\"nameRequired\":\"Name is a required field\",\"nameMax\":\"The maximum number of characters is 255\",\"emailRequired\":\"Email is a required field\",\"emailInvalid\":\"Invalid email\",\"emailMax\":\"The maximum number of characters is 255\",\"phoneRequired\":\"Phone is a required field\",\"phoneInvalid\":\"Phone number contains invali
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Follow us for the latest on technology, business, and more.\"}},\"corporate_websites\":{\"banner\":{\"title\":\"Corporate website development\",\"description\":\"Moai Team crafts high-performance corporate websites built for modern businesses ready to make an impact online.\",\"buttonText\":\"Schedule a free consultation\",\"alt\":\"Moai Team, corporate websites development services\"},\"linksPath\":{\"services\":\"Services\",\"corporateWebsiteDevelopment\":\"Corporate website development\"},\"whatIs\":{\"title\":\"What does website development include?\",\"description\":\"Website development isn’t just about writing code - it’s about crafting a digital experience that supports your business goals from every angle. At Moai Team we provide everything you need to build a site that works smarter, looks better and delivers measurable results.\"},\"areasOfExpertise\":{\"description\":\"We offer a full suite of services to ensure your corporate website isn’t just beautiful. 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264olYard\":\"KoolYard is a specialized marketplace providing food and products tailored specifically for school communities. The platform connects students, parents, and educators with reliable vendors, streamlining purchases and fostering community interaction.\",\"smartphoneKey\":\"SmartphoneKey transforms your mobile device into a convenient digital intercom solution. This innovative app allows seamless, secure entry into residential buildings directly from smartphones, enhancing user convenience and building security.\",\"freshAndFurious\":\"Fresh \u0026 Furious is an engaging website for a healthy food restaurant renowned for fast delivery service. 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264ontact you to ask for your consent.\"}]},{\"title\":\"Third parties\",\"id\":\"Third parties\",\"content\":[{\"key\":\"p\",\"text\":\"We carefully select third-party service providers that are committed to maintaining your sensitive information with the utmost security. We use exceptional services that claim they are compliant with GDPR and other data protection regulations. You can contact us at [email protected] for more information privacy-policy our data protection measures.\"}]},{\"title\":\"Storage period\",\"id\":\"Storage period\",\"content\":[{\"key\":\"p\",\"text\":\"We store your personal data for a limited period of time, which varies depending on type of data, the purposes of processing, and legal requirements. To obtain information privacy-policy the storage period of your personal data, please contact us via [email protected].\"}]},{\"title\":\"How to withdraw your consent for collecting, processing, and using your data\",\"id\":\"How to withdraw your consent for collecting, processing, and using your data\",\"content\":[{\"key\":\"p\",\"text\":\"You may withdraw your consent for collecting, processing, and using your data by sending an email to [email protected]. You may withdraw your consent to receive emails at any time by clicking Unsubscribe on any email or replying with a request to unsubscribe.\"}]},{\"title\":\"Your rights\",\"id\":\"Your rights\",\"content\":[{\"key\":\"p\",\"text\":\"As a data subject you have the following rights:\"},{\"key\":\"list\",\"listData\":[{\"itemData\":[{\"key\":\"text\",\"text\":\"to find out what data is being collected and processed, how it is being used, for how long it is being stored;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"to obtain from us the erasure of your personal data when this personal data is no longer necessary in relation to the purposes for which it was collected or otherwise processed;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"to restrict us from processing your data;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"to obtain information in relation to your personal data;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"to access your personal data;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"to withdraw the consent;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"to correct mistakes in your personal data;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"right to erase your personal data.\"}]}]}]},{\"title\":\"How you can raise a complaint\",\"id\":\"How you can raise a complaint\",\"content\":[{\"key\":\"p\",\"text\":\"You can raise a complaint by sending an email to [email protected].\"}]},{\"title\":\"Privacy Policy updates\",\"id\":\"Privacy Policy updates\",\"content\":[{\"key\":\"p\",\"text\":\"We will update this Privacy Policy from time to time to reflect changes in our practices, security standards, applicable laws, third-party services or tools that we use. We will update this Privacy Policy as we consider it necessary.\"}]}]},\"restaurants-kit\":{\"banner\":{\"title\":\"Launch Your Digital Restaurant. Fast. Flexible. 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264in the\",\"policy\":\"Privacy Policy\",\"accept\":\"Accept\",\"decline\":\"Decline\",\"acceptTitle\":\"Accept cookie\",\"declineTitle\":\"Decline cookie\"},\"header\":{\"contact\":\"Contact Us\",\"all\":\"All\",\"services\":\"Services\",\"solutionKits\":\"Solution Kits\",\"company\":\"Company\",\"blog\":\"Blog\"},\"nav\":{\"agentic\":{\"ecosystem\":\"AgenticProduct Ecosystem\",\"standard\":\"Agentic Product Standard\",\"agenticmind\":\"AgenticMind\",\"agenticops\":\"AgenticOps\",\"performance\":\"AgenticPerformance\",\"selfhealing\":\"AgenticSelfHealingCode\",\"gateway\":\"AgenticGateway\",\"assurance\":\"AgenticAssurance\",\"development\":\"Agentic software development\"},\"sections\":{\"agentic\":\"Agentic Software\",\"services\":\"Services\",\"company\":\"Company\",\"kits\":\"Solution Kits\"},\"kits\":{\"restaurant\":\"Moai Team Restaurant Toolkit\",\"marketplace\":\"Moai Team Marketplace Toolkit\",\"learning\":\"Moai Team Learning Toolkit\",\"real_estate\":\"Moai Team Real Estate Toolkit\"},\"services\":{\"custom\":\"Custom software development\",\"reengineering\":\"Software reengineering\",\"uxui\":\"UX/UI design\",\"corporate\":\"Corporate websites\",\"ai\":\"AI software development\",\"chatbot\":\"Chatbot development\"},\"company\":{\"about\":\"About\",\"portfolio\":\"Portfolio\",\"blog\":\"Blog\",\"partnership\":\"Partnership program\"}},\"footer\":{\"slogan\":\"Moai Team helps business owners leverage technology to achieve their goals. We build partnerships, not just code.\",\"allRightsReserved\":\"© 2025 Moai Team. All rights reserved.\",\"termsOfService\":\"Terms of service\",\"privacyPolicy\":\"Privacy policy\"},\"banner\":{\"shedule_button\":\"Schedule a free consultation\",\"shedule_big_button\":\"Schedule a call\"},\"get_in_touch_form\":{\"placeholders\":{\"name\":\"Write your name\",\"email\":\"Write your email\",\"phone\":\"Write your phone\",\"message\":\"Please let us know if you have any questions\"},\"labels\":{\"name\":\"Name\",\"email\":\"Email\",\"phone\":\"Phone\",\"message\":\"Message\",\"privacyPolicy\":\"I accept your\",\"privacyPolicyLink\":\"Privacy Policy\",\"sendButton\":\"Send\",\"errors\":{\"required\":\"is a required field\",\"maxLength\":\"The maximum number of characters is\",\"invalidEmail\":\"Invalid email\",\"phoneInvalidChars\":\"Phone number contains invalid characters\",\"phoneTooShort\":\"Phone number is too short\",\"phoneTooLong\":\"Phone number is too long\",\"mustBeChecked\":\"Field must be checked\",\"saveError\":\"An error occurred while saving the form\"},\"getInTouch\":\"Get in touch\"}},\"portfolio_slider\":{\"title\":\"Proud to Work With\",\"description\":\"We partner with global brands and fast-growing innovators to deliver software that works\",\"learn_more\":\"Learn more\",\"see_more\":\"See More\"},\"faq\":{\"title\":\"FAQ\",\"description\":\"To help you with any questions that are not listed here, we offer free, no-obligation consultations\"},\"softwareDevelopmentLifeCycle\":{\"titleFallback\":\"Software development life cycle\",\"descriptionFallback\":\"SDLC provides a structured approach to software development that helps to ensure that all aspects of the project are addressed in a coordinated way\"},\"terms\":{\"list\":[{\"title\":\"Terms\",\"id\":\"terms-of-service\",\"content\":[{\"key\":\"p\",\"text\":\"Your access to this website is conditioned on your acceptance of and compliance with these Terms. These Terms apply to all visitors, users and others who access or use this website. If you do not agree with any of these Terms, you are prohibited from using or accessing this website. The materials contained in this website are protected by applicable copyright and trade mark law.\"}]},{\"title\":\"Use License\",\"id\":\"useLicense\",\"content\":[{\"key\":\"p\",\"text\":\"Permission is granted to temporarily download one copy of the materials for personal, non-commercial transitory viewing only. This is the grant of a license, not a transfer of title, and under this license you may not:\"},{\"key\":\"list\",\"listData\":[{\"itemData\":[{\"key\":\"text\",\"text\":\"modify or copy the materials;\"}]},{\"itemData\":[{\"key\":\"text\",\"text\":\"use the materials for any commercial and n
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The Agentic Product Standard is our field-tested, openly published reference for that work, plus a Claude Code skill set that puts it directly into your editor.\",\"areas_of_expertise_description\":\"A complete, opinionated playbook — from first principles to a Definition of Done — that any team can adopt today.\",\"areas_of_expertise_list\":[{\"title\":\"The five principles\",\"description\":\"Determinism by default, agency by necessity. Architecture beats framework. Harness over model. Context engineering as the core discipline. Eval-driven development as non-negotiable. The spine of every decision in the standard.\"},{\"title\":\"The Autonomy Ladder\",\"description\":\"Every degree of autonomy must be earned, not granted upfront. A concrete ladder for deciding how much freedom an agent gets — and what guardrails each rung requires.\"},{\"title\":\"Five composition patterns\",\"description\":\"Reusable structures for wiring agents together that outlive any single framework — so your design survives the next library churn.\"},{\"title\":\"The 7-layer harness\",\"description\":\"98% of reliability lives in the code around the LLM. A layer-by-layer map of that harness: the constant you invest in while the model stays the variable.\"},{\"title\":\"Claude Code skill set\",\"description\":\"agent-builder for shipping one production-grade agent and agentic-product-architect for multi-agent products — skills that auto-load the right guidance while you design, build and review.\"},{\"title\":\"Checklist \u0026 Definition of Done\",\"description\":\"A 10-question decision checklist and a 12-point production-readiness audit, so “done” means the same thing for every agent you ship.\"}],\"faq\":[{\"title\":\"Is the standard really open-source?\",\"description\":\"Yes. The Agentic Product Standard is published under the MIT license on GitHub (Moai Team-Team-LLC/agentic-product-standard). You can read it, fork it, and adopt it commercially with no strings attached.\"},{\"title\":\"Who is it for?\",\"description\":\"Engineering teams and founders building products where an LLM directs part of the process — anyone who has shipped an agent demo and now needs it to survive production.\"},{\"title\":\"How do I start using it?\",\"description\":\"Read STANDARD.md once, then install the Claude Code skills with a single command (npx skills add Moai Team-Team-LLC/agentic-product-standard). The skills load the right guidance as you design, build and review.\"},{\"title\":\"How does it relate to AgenticMind?\",\"description\":\"The standard tells you how; AgenticMind is the flagship reference implementation you can run — an auditable, self-improving knowledge \u0026 memory layer that plugs into the standard over MCP.\"},{\"title\":\"Why did Moai Team publish it?\",\"description\":\"Because architecture is 
264what remains when the model improves. We codified the practices we use to ship reliable agents, and opened them so the whole field can build on a common, battle-tested foundation.\"},{\"title\":\"Can Moai Team apply it to my product?\",\"description\":\"Yes — we design, build and review agentic products against this exact standard. Reach out below and we will map it to your use case.\"}],\"get_in_touch_description\":\"Want to apply the Agentic Product Standard to your product? Let’s talk.\"},\"agenticmind\":{\"banner_title\":\"AgenticMind\",\"banner_description\":\"The auditable, self-improving knowledge \u0026 memory layer for AI agents. Grounded answers with provable citations, a full why-trace for every answer, and a corpus that improves itself — served to any agent over MCP. Zero-key, multilingual and self-hostable on Postgres alone. Open source under Apache 2.0.\",\"banner_alt\":\"Moai Team, AgenticMind knowledge and memory layer\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"AgenticMind\",\"what_is_title\":\"What is AgenticMind?\",\"what_is_description\":\"Not “memory storage for an agent.” AgenticMind is the substrate an agent points at when it needs answers it can trust, a trail it can audit, and a knowledge base that compounds. Most agent memory is a vector store with save() and search() — that buys fuzzy recall and zero accountability: you can’t tell why an answer came back, whether it’s current, or whether a source even supports it. AgenticMind treats knowledge as a first-class, auditable, self-improving substrate, and exposes it to any agent over the Model Context Protocol.\",\"areas_of_expertise_description\":\"Everything plain RAG and memory SDKs leave out — accountability, provenance and a corpus that gets better with use.\",\"areas_of_expertise_list\":[{\"title\":\"Citation-enforced answers\",\"description\":\"Every claim in an answer is keyed to a numbered source. No source, no claim — and the unsupported half of a question is refused, not fabricated.\"},{\"title\":\"Fully auditable why-trace\",\"description\":\"A replayable trace for every answer: what was retrieved, ranked and used, with phases, model and a telemetry id you can open later.\"},{\"title\":\"Self-improving corpus\",\"description\":\"Validated answers are promoted back into the corpus by a judge-gated compounding loop driven by programmatic signals — not human thumbs.\"},{\"title\":\"Tiered retrieval\",\"description\":\"Chunks → typed fact cards → knowledge graph; hybrid vector + full-text, recency-aware ranking for answers that stay current.\"},{\"title\":\"Safe by construction\",\"description\":\"Scoped, least-privilege MCP tokens, fail-closed auth, and guardrails on both input and output.\"},{\"title\":\"One datastore\",\"description\":\"Postgres + pgvector carries vectors, full-text, the graph and the durable queue. No Redis, no Neo4j, no vector-DB sprawl.\"}],\"faq\":[{\"title\":\"How is it different from a vector store?\",\"description\":\"A vector store gives fuzzy recall and no accountability. AgenticMind enforces citations, ships a replayable why-trace, verifies relations through a graph module, and compounds validated knowledge back into the corpus.\"},{\"title\":\"What license is it under?\",\"description\":\"Apache 2.0, on GitHub (Moai Team-Team-LLC/AgenticMind). Free to use, self-host and build on, including commercially.\"},{\"title\":\"What do I need to run it?\",\"description\":\"Postgres with pgvector, and Node or Bun. One datastore, no Redis or Neo4j. It’s zero-key and multilingual by default, and self-hostable end to end.\"},{\"title\":\"How does an agent use it?\",\"description\":\"AgenticMind is headless and exposes its engine as MCP tools (search, ask, ingest, recall, write, signal). Any MCP-capable agent points at it over streamable HTTP with scoped bearer auth.\"},{\"title\":\"How does it relate to the Agentic Product Standard?\",\"description\":\"AgenticMind is the flagship reference implementation of the standard — the open-source pick for the memory/knowledge slot, implementing the canon layer by layer.\"},{\"title\":\"Can Moai Team deploy and integrate it for us?\",\"description\":\"Yes. We self-host, tune and wire AgenticMind into your agents as part of our agentic software development work. Reach out below.\"}],\"get_in_touch_description\":\"Want a knowledge \u0026 memory layer your agents can actually trust? Let’s talk.\"},\"agenticops\":{\"banner_title\":\"AgenticOps\",\"banner_description\":\"The runtime \u0026 operations layer agentic fleets run on. Deploy agents as versioned manifests, schedule them across a fleet without double-runs, give them a durable backlog and bounded, observable execution — with inter-agent calls governed by a default-deny policy. Lean and Bun-native, the reference implementation of the Agentic Product Standard’s Fleet operations surface. Open source under Apache 2.0.\",\"banner_alt\":\"Moai Team, AgenticOps runtime and operations layer\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"AgenticOps\",\"what_is_title\":\"What is AgenticOps?\",\"what_is_description\":\"Not “a framework for writing agents” — it is the layer that runs them. The Agentic Product Standard tells you how to build one correct agent or system: durable execution, observability, security. AgenticOps implements what comes after — running many long-lived agents as deployed infrastru
264cture. An agent becomes a versioned, deployable manifest; a coordinated scheduler fires it across replicas without double-runs; a durable backlog survives restarts; every run is bounded and cancellable; and a default-deny matrix governs which agent may call which. The operations plane, kept deliberately lean — a runner is a function with limits, not a platform.\",\"areas_of_expertise_description\":\"Everything you need to run an agent fleet in production — and nothing you don’t.\",\"areas_of_expertise_list\":[{\"title\":\"Agent as a deployable manifest\",\"description\":\"Each agent is a versioned manifest — limits, runtime, schedule, env — distinct from its design-time contract. The same definition runs in dev and prod, with agent logic kept separate from the platform prompt.\"},{\"title\":\"Coordinated scheduling\",\"description\":\"Fleet cron with a fire-once lock across replicas and misfire handling — runs missed while the fleet was down are coalesced, never silently dropped.\"},{\"title\":\"Durable backlog\",\"description\":\"A SQLite-backed FIFO that survives restarts. Tasks are claimed under a lease; a dead worker’s lease expires and the work is re-claimed, so nothing is lost.\"},{\"title\":\"Bounded execution\",\"description\":\"Every run carries an explicit max-turns ceiling, a wall-clock timeout and graceful cancellation. No unbounded loops, ever.\"},{\"title\":\"Fleet observability\",\"description\":\"An append-only operational audit of lifecycle, auth and tool events, plus per-agent heartbeat and health — layered on per-run traces, OTel-ready.\"},{\"title\":\"Inter-agent policy\",\"description\":\"A default-deny “who may call whom” matrix derived from each manifest, enforced fail-closed on a real delegation path with a full audit trail.\"}],\"faq\":[{\"title\":\"How is it different from an agent framework?\",\"description\":\"Frameworks help you write an agent. AgenticOps runs many of them as infrastructure — scheduling, a durable backlog, bounded execution, fleet observability and inter-agent policy. It wraps any runtime (Claude Agent SDK, Claude Code, Gemini) rather than replacing it.\"},{\"title\":\"What license is it under?\",\"description\":\"Apache 2.0, on GitHub (Moai-Team-LLC/AgenticOps). Free to use, self-host and build on, including commercially.\"},{\"title\":\"What do I need to run it?\",\"description\":\"It’s Bun-native — it uses bun:sqlite and bun:test, so it runs on Bun ≥ 1.1 with no build step. One embedded datastore: no Redis, no external queue.\"},{\"title\":\"How does it relate to AgenticMind?\",\"description\":\"They’re complementary reference implementations of the Standard: AgenticMind is the knowledge \u0026 memory layer agents think with; AgenticOps is the runtime \u0026 operations layer they run on.\"},{\"title\":\"How does it relate to the Agentic Product Standard?\",\"description\":\"AgenticOps is the reference implementation of the Standard’s Fleet operations surface — manifest, scheduling, durable backlog, bounded execution, observability and inter-agent policy, mapped to code.\"},{\"title\":\"Can Moai Team deploy and operate it for us?\",\"description\":\"Yes. We wire AgenticOps into your agents and stand up the operations plane as part of our agentic software development work. Reach out below.\"}],\"get_in_touch_description\":\"Want to run your agents like infrastructure, not a demo? Let’s talk.\"},\"agentic-software-development\":{\"banner_title\":\"Agentic software development services\",\"banner_description\":\"We design and ship production-grade agentic products — systems where an LLM directs part of the process inside a deterministic architecture with explicit trust boundaries. From autonomy design and harness engineering to tools, memory and eval-driven development, we build agents that survive contact with production.\",\"banner_alt\":\"Moai Team, agentic software development services\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"Agentic software development\",\"what_is_title\":\"What is agentic software development?\",\"what_is_description\":\"Agentic software development is the practice of building products where part of the process is dynamically directed by an LLM — within a deterministic architecture and explicit trust boundaries, not a free-for-all. The hard part is rarely the model; it is the harness, the context engineering and the eval discipline around it. We work to our own openly published Agentic Product Standard, so every agent we ship has earned its autonomy, has a measurable Definition of Done, and keeps working when the model underneath it changes.\",\"areas_of_expertise_description\":\"End-to-end agentic engineering, grounded in the Agentic Product Standard and proven in production.\",\"areas_of_expertise_list\":[{\"title\":\"Agent architecture \u0026 autonomy design\",\"description\":\"We map your problem onto the autonomy ladder, decide single- vs multi-agent, and choose the composition pattern — so freedom is earned and bounded, not assumed.\"},{\"title\":\"Harness engineering\",\"description\":\"The 7 layers around the LLM loop — routing, validation, guardrails, retries, observability — where reliability actually lives.\"},{\"title\":\"Tools \u0026 MCP integration\",\"description\":\"Well-scoped, least-privilege tools exp
264osed over the Model Context Protocol, with sandboxing and fail-closed auth.\"},{\"title\":\"Memory \u0026 knowledge layers\",\"description\":\"Auditable, citation-enforced retrieval and memory — including AgenticMind, our open-source knowledge \u0026 memory layer on Postgres + pgvector.\"},{\"title\":\"Eval-driven development\",\"description\":\"An eval pyramid, judge calibration and trace review, so we measure before we ship and improve from real behaviour, not vibes.\"},{\"title\":\"Durable, multi-agent orchestration\",\"description\":\"Workflow + activity patterns for long-running, recoverable agent processes that don’t lose state when something fails.\"}],\"life_cycle_title\":\"Our agentic development process\",\"life_cycle_description\":\"An iterative, eval-first process: we earn autonomy step by step, instrument everything, and harden before we hand over.\",\"life_cycle_steps\":[{\"title\":\"Discovery \u0026 autonomy design\",\"description\":\"We start from the problem, not the model: what must be deterministic, where an LLM genuinely adds value, and how much autonomy each step has earned. The output is a clear agent contract, trust boundaries and a measurable Definition of Done.\",\"alt\":\"Agentic software development process, step one, discovery and autonomy design\",\"image\":\"/images/ai-cycle-1.png\"},{\"title\":\"Architecture \u0026 harness\",\"description\":\"We design the deterministic architecture and the 7-layer harness around the LLM loop — routing, validation, guardrails, retries and observability — choosing single- vs multi-agent and the right composition pattern.\",\"alt\":\"Agentic software development process, step two, architecture and harness\",\"image\":\"/images/ai-cycle-2.png\"},{\"title\":\"Build \u0026 integrate\",\"description\":\"We implement the agent, wire up well-scoped tools over MCP, and connect an auditable memory \u0026 knowledge layer. Context engineering and citation-enforced retrieval keep answers grounded and current.\",\"alt\":\"Agentic software development process, step three, build and integrate\",\"image\":\"/images/ai-cycle-3.png\"},{\"title\":\"Evaluate \u0026 harden\",\"description\":\"We build the eval pyramid, calibrate judges and review traces, then close the loop on cost, latency and failure modes — so the system is production-ready, observable and recoverable before launch.\",\"alt\":\"Agentic software development process, step four, evaluate and harden\",\"image\":\"/images/ai-cycle-4.png\"}],\"faq\":[{\"title\":\"What is an agentic product, exactly?\",\"description\":\"A product where part of the process is dynamically directed by an LLM inside a deterministic architecture with explicit trust boundaries — not a chatbot bolted onto a UI. Autonomy is earned per step and always bounded.\"},{\"title\":\"Which models and frameworks do you use?\",\"description\":\"We are framework-agnostic and pick per task: Claude, OpenAI and others for models; LangGraph, the Claude Agent SDK or OpenAI Agents SDK for orchestration. Architecture beats framework, so the design outlives the tools.\"},{\"title\":\"How do you make agents reliable?\",\"description\":\"98% of reliability lives in the harness around the model. We invest in the 7-layer harness, context engineering and eval-driven development, with trace review and a 12-point production-readiness audit before launch.\"},{\"title\":\"Do you work to a standard?\",\"description\":\"Yes — our own openly published Agentic Product Standard (MIT). Every engagement is designed, built and reviewed against it, so quality is consistent and auditable.\"},{\"title\":\"Can you add agents to an existing product?\",\"description\":\"Absolutely. We integrate agents, tools (MCP) and a memory/knowledge layer into existing systems, with guardrails and evals so the new autonomy is safe and measurable.\"},{\"title\":\"How do we get started?\",\"description\":\"Book a free consultation below. We’ll scope the problem, map it onto the autonomy ladder, and propose an architecture and a Definition of Done.\"}],\"get_in_touch_description\":\"Ready to build an agentic product that survives production? Let’s talk.\"},\"agentic-links\":{\"section_title\":\"Explore Agentic Software\",\"section_description\":\"More from our agentic practice — the standard we build to, the open-source layers that implement it, and the team that ships it.\",\"cards\":{\"ecosystem\":{\"title\":\"The AgenticProduct Ecosystem\",\"description\":\"One standard and six open-source reference implementations that close the loop production agents need: run → remember → measure → heal.\"},\"standard\":{\"title\":\"Agentic Product Standard\",\"description\":\"The canonical open standard for production-grade agentic products — autonomy ladder, the 7-layer harness and eval discipline.\"},\"agenticmind\":{\"title\":\"AgenticMind\",\"description\":\"Our open-source, auditable, self-improving knowledge \u0026 memory layer for AI agents, served over MCP.\"},\"agenticops\":{\"title\":\"AgenticOps\",\"description\":\"Our open-source runtime \u0026 operations layer for agentic fleets — scheduling, a durable backlog, bounded execution and fleet observability.\"},\"performance\":{\"title\":\"AgenticPerformance\",\"description\":\"Our open-source evals \u0026 observability layer: OTel traces, golden-set evals with a CI gate, failure cluster
264s and a governed improvement loop.\"},\"selfhealing\":{\"title\":\"AgenticSelfHealingCode\",\"description\":\"Our open-source self-healing ops layer: grounded incident diagnosis, signed signal intake and repair on earned autonomy.\"},\"gateway\":{\"title\":\"AgenticGateway\",\"description\":\"Our open-source model \u0026 cost plane: one key for the fleet, eval-measured routing, fail-closed cost ceilings and an evidence trail.\"},\"assurance\":{\"title\":\"AgenticAssurance\",\"description\":\"Our open-source security \u0026 assurance layer: red-teams any agent against the OWASP Agentic Top 10 and MITRE ATLAS, with SARIF output for CI.\"},\"development\":{\"title\":\"Agentic software development\",\"description\":\"We design and ship production-grade agentic products end to end — architecture, tools, memory and evals.\"}}},\"agenticperformance\":{\"banner_title\":\"AgenticPerformance\",\"banner_description\":\"The evals \u0026 observability layer for agentic products. Instrument any LLM-agent system over OpenTelemetry and turn raw execution into traces you can reason about, per-agent golden-set evals with a CI gate, named failure clusters with trends, and a governed improvement loop with hard safety boundaries. Engine-agnostic; the reference implementation of the Agentic Product Standard's Evals \u0026 observability surface. Open source under Apache 2.0.\",\"banner_alt\":\"Moai Team, AgenticPerformance evals and observability layer\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"AgenticPerformance\",\"what_is_title\":\"What is AgenticPerformance?\",\"what_is_description\":\"No measurement, no improvement — the Standard makes eval-driven development non-negotiable, and AgenticPerformance (APL) is the layer that implements it. It instruments any agent stack — LangGraph, CrewAI, the OpenAI or Claude Agent SDKs, or a raw agent loop — over OpenTelemetry, normalizing OpenInference and gen_ai.* conventions into one canonical model. On top of that trace store it runs per-agent golden-set evals with a version gate that blocks regressions in CI, auto-triages failures into stable, run-over-run clusters with significance-gated trends, and drives a governed improvement loop — assisted, suggested, or judge-gated automatic — inside a mechanically enforced safety envelope with a rollback-able ledger. Every agent gets a headless scorecard, exposed as API and MCP.\",\"areas_of_expertise_description\":\"Everything you need to measure and improve an agent fleet — from raw spans to a governed improvement loop.\",\"areas_of_expertise_list\":[{\"title\":\"OTel-native tracing\",\"description\":\"Built on the OpenTelemetry GenAI conventions with a normalization layer that maps OpenInference and gen_ai.* into one canonical model — instrument once, reason about every run.\"},{\"title\":\"Golden-set evals with a CI gate\",\"description\":\"A mandatory deterministic baseline suite plus a per-agent golden set, and a version gate that blocks any new agent version that regresses against the prior one on a frozen set.\"},{\"title\":\"Failure clusters and trends\",\"description\":\"Failed runs are auto-triaged into named, stable failure clusters that persist run over run, with trends that only fire when statistically significant — no noise-chasing.\"},{\"title\":\"Governed improvement loop\",\"description\":\"Three autonomy levels — assisted, suggested and judge-gated automatic — inside a mechanically enforced safety envelope: a diff allowlist, a content guard and a fully justified, rollback-able improvement ledger.\"},{\"title\":\"Immutable agent registry\",\"description\":\"Agents and content-addressed, immutable agent versions — so every trace, eval and improvement is pinned to exactly the version that produced it.\"},{\"title\":\"Per-agent scorecard\",\"description\":\"A headless read model of each agent's health — eval scores, failure clusters, trends — exposed as an API and over MCP, ready for dashboards and fleet views.\"}],\"faq\":[{\"title\":\"Does it require a specific agent framework?\",\"description\":\"No. APL is engine-agnostic: it instruments LangGraph, CrewAI, the OpenAI and Claude Agent SDKs, or a hand-rolled agent loop over OpenTelemetry. If your stack can emit OTLP, APL can measure it.\"},{\"title\":\"What license is it under?\",\"description\":\"The core is Apache 2.0, on GitHub (Moai-Team-LLC/AgenticPerformance). Enterprise features — SSO/RBAC, audit, fleet view, on-prem — are a separate edition.\"},{\"title\":\"How does it relate to the Agentic Product Standard?\",\"description\":\"It is the reference implementation of the Standard's Evals \u0026 observability surface: traces, golden-set evals with a CI gate, error analysis and a governed improvement loop, exactly as the Standard prescribes them.\"},{\"title\":\"How does it relate to AgenticMind and AgenticOps?\",\"description\":\"They're complementary: AgenticOps runs the fleet, AgenticMind gives agents knowledge \u0026 memory, and AgenticPerformance measures every run — with AgenticSelfHealingCode repairing what breaks, closing the run → remember → measure → heal loop.\"},{\"title\":\"What do I need to run it?\",\"description\":\"A Postgres/TimescaleDB instance as the single datastore and an OTLP endpoint — the ingest server accepts standard OpenTelemetry traces, and tenant isolation is enforced with row-level security.\"}],\"get_in_touch_description\":\"Want eval-driven development wired 
264into your agent stack? Tell us about your product and we'll show you what APL can measure from day one.\"},\"agenticselfhealingcode\":{\"banner_title\":\"AgenticSelfHealingCode\",\"banner_description\":\"Self-healing ops for agentic products — a network of agents for production monitoring, incident diagnosis and repair, designed adversarially and reduced to the parts that are actually safe to build first. Grounded RCA that never guesses, signed signal intake, injection-resistant telemetry handling and autonomy that is earned, not granted. Runs standalone on Postgres + pgvector. Open source under Apache 2.0.\",\"banner_alt\":\"Moai Team, AgenticSelfHealingCode self-healing operations layer\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"AgenticSelfHealingCode\",\"what_is_title\":\"What is AgenticSelfHealingCode?\",\"what_is_description\":\"Every production system breaks; the question is what happens next. AgenticSelfHealingCode closes the heal step of the run → remember → measure → heal loop: it ingests production signals over a signed, HMAC-verified boundary — native Sentry webhooks included — grounds incident diagnosis in real evidence like deploy timelines and repo history, and either recommends the fix with a confirmed verdict or escalates with the named missing evidence. It never guesses. Telemetry content is treated as data, never as instructions, so a prompt-injection attempt inside a stack trace is flagged instead of obeyed; duplicate incidents don't double-page the on-call; and every automatic action passes an earned-autonomy gate with a kill switch and an auto-action ledger. Durable incident memory lives in Postgres + pgvector and survives restarts.\",\"areas_of_expertise_description\":\"Everything an on-call team wants from a healing agent — and every guardrail a security team demands.\",\"areas_of_expertise_list\":[{\"title\":\"Grounded diagnosis \u0026 RCA\",\"description\":\"Deploy-linked root-cause analysis grounded in the actual repo and deploy timeline — it recommends a rollback with evidence, or escalates naming exactly what evidence is missing. Never a guess.\"},{\"title\":\"Signed signal intake\",\"description\":\"Every inbound signal crosses an HMAC-signed boundary; spoofed webhooks are rejected at ingestion, and Sentry is supported natively — no shim.\"},{\"title\":\"Injection-resistant telemetry\",\"description\":\"Telemetry is data, never instructions: a prompt-injection attempt inside an exception message is flagged as a finding, not executed as a command.\"},{\"title\":\"Earned autonomy\",\"description\":\"Automatic actions live behind an autonomy ladder with human confirmation first, an append-only auto-action ledger, and a signed kill switch that stops everything instantly.\"},{\"title\":\"On-call-friendly operations\",\"description\":\"Duplicate incidents are deduplicated instead of double-paging, delivery lands in Telegram with tap-to-ack, and the whole service is operable over HTTP — status, incidents, kill and release.\"},{\"title\":\"Durable incident memory\",\"description\":\"Incident memory, notify state and the action ledger live in Postgres + pgvector and survive restarts — the system remembers what broke and what fixed it.\"}],\"faq\":[{\"title\":\"Does it auto-apply fixes?\",\"description\":\"Only as far as autonomy has been earned. The design is adversarial-first: repair proposals start human-confirmed, every automatic action is gated, logged in a ledger, and behind a kill switch. Autonomy is a ladder, not a default.\"},{\"title\":\"What do I need to try it?\",\"description\":\"Just Bun: bun run demo starts the real signal-intake service and plays five scenarios at it over real HTTP with real HMAC signatures — no keys, no database, no config. For real use, add Postgres + pgvector, your Sentry and an Anthropic key for grounded diagnosis.\"},{\"title\":\"What license is it under?\",\"description\":\"Apache 2.0, on GitHub (Moai-Team-LLC/AgenticSelfHealingCode). Free to use, self-host and build on, including commercially.\"},{\"title\":\"How does it fit the ecosystem?\",\"description\":\"It closes the heal step of run → remember → measure → heal: AgenticOps runs the fleet, AgenticMind remembers, AgenticPerformance measures, and AgenticSelfHealingCode repairs what breaks. Sibling integrations are optional adapters, never hard dependencies.\"},{\"title\":\"Why \\\"designed adversarially\\\"?\",\"description\":\"The full design was red-teamed on paper first, then deliberately reduced to the subset that is safe to operate today — grounded diagnosis, signed intake, gated actions — with the more autonomous loops deferred until they can be earned.\"}],\"get_in_touch_description\":\"Tired of 3am pages an agent could have triaged? Tell us about your production stack and we'll map what's safe to automate first.\"},\"agenticgateway\":{\"banner_title\":\"AgenticGateway\",\"banner_description\":\"The model \u0026 cost plane for agentic products. One OpenAI-compatible key for the whole fleet, with measured routing chosen from eval runs, fail-closed cost ceilings, semantic caching and an evidence trail behind every call — on a Bifrost data plane. The reference implementation of the Agentic Product Standard's Model \u0026 provider and Cost \u0026 FinOps surfaces. Open source under Apache 2.0.\",\"banner_alt\":\"Moai Team, AgenticGateway model and cost plane\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"AgenticGateway\",\"what_is_title\":\"What is AgenticGateway?\",\"what_is_description\":\"Most stacks leave the model boundary open: hand-picked models, budgets as dashboards you read after the money is gone, provider lock-in. AgenticGateway closes exactly that. Agents call one OpenAI-compatible endpoint with one key; tenants, budgets, upstream credentials and routing all derive from it server-side. Routing aliases resolve against a policy built from an AgenticPerformance eval run — change the eval run and you change the routing, with the eval run recorded for audit. Cost is a circuit breaker, not a report: tenant-window and per-run ceilings deny fail-closed the moment they trip, and every call lands in a ledger and as a cost-attributed OpenTelemetry span. Transport — failover, load-balancing, retries, streaming, semantic caching — is Bifrost's job, with microsecond overhead; adding a provider is config, not code.\",\"areas_of_expertise_description\":\"The two surfaces most agent stacks leave open — model \u0026 provider and cost \u0026 FinOps — implemented properly, and nothing else.\",\"areas_of_expertise_list\":[{\"title\":\"One key for the fleet\",\"description\":\"Agents call one OpenAI-compatible endpoint with a single gateway key; tenants, budgets, upstream credentials and routing derive from it server-side, and client keys are stored as sha256 only.\"},{\"title\":\"Measured routing\",\"description\":\"Route aliases resolve against a policy built from an AgenticPerformance eval run — the ranking becomes the failover chain, the eval run id is recorded, and no LLM sits on the hot path.\"},{\"title\":\"Fail-closed cost ceilings\",\"description\":\"Tenant-window and per-run ceilings are enforced in code and deny the moment they trip — cost control as a circuit breaker, not a monthly report.\"},{\"title\":\"Bifrost data plane\",\"description\":\"Provider abstraction, failover, load-balancing, retries, streaming and semantic caching are handled by Bifrost with microsecond overhead; adding a provider is configuration, not code.\"},{\"title\":\"Evidence trail\",\"description\":\"Every call lands in a hash-not-text ledger and as a cost-attributed OTel span in AgenticPerformance's conventions — cost-per-outcome in the same traces as your evals.\"},{\"title\":\"Guardrails on the model boundary\",\"description\":\"AgenticMind's guard runs inline, protected-path tool calls are checked against AgenticAssurance's policy pack, and a sampled judge reviews high-risk routes.\"}],\"faq\":[{\"title\":\"How is it different from a plain LLM proxy?\",\"description\":\"A proxy forwards calls. AgenticGateway is a policy plane: routing chosen from measured eval runs, cost ceilings enforced fail-closed in code, guardrails on the boundary and an auditable evidence trail — the Standard's Model \u0026 provider and Cost \u0026 FinOps surfaces, implemented.\"},{\"title\":\"What does Bifrost do underneath?\",\"description\":\"Everything transport: provider abstraction, failover, load-balancing, retries, streaming and semantic caching, with microsecond overhead. AgenticGateway never re-implements transport — adding a provider is config, not code.\"},{\"title\":\"What license is it under?\",\"description\":\"Apache 2.0, on GitHub (Moai-Team-LLC/AgenticGateway). 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Given a capability manifest and a runner adapter, it red-teams any agent: an attack library mapped to the OWASP Agentic Top 10 and MITRE ATLAS, a toxic-flow graph that finds lethal-trifecta and RCE composition paths, detection of side-effect divergences — and SARIF output that gates CI. Framework-neutral, published on 
264npm. Open source under MIT.\",\"banner_alt\":\"Moai Team, AgenticAssurance security and assurance layer\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"AgenticAssurance\",\"what_is_title\":\"What is AgenticAssurance?\",\"what_is_description\":\"Single-prompt scanners miss the attacks that matter for agents — the ones composed across tools. AgenticAssurance (AAL) is the framework-neutral offensive core of the Agent Assurance Layer: you describe your agent in a capability manifest — its tools and their side-effect classes, data scopes, untrusted-ingress points and declared mitigations — plug in a runner adapter, and it attacks an isolated copy of the agent. It runs an attack library mapped to the OWASP Top 10 for Agentic Applications and MITRE ATLAS, builds a toxic-flow graph over declared tools to find lethal-trifecta and untrusted-content-to-code-execution paths, and catches execution-layer divergences where the agent refuses in text but a side-effecting tool still fires. Findings ship as SARIF 2.1.0 for code scanning plus a human-readable report, and a failing verdict exits non-zero — so red-teaming gates CI like any other test.\",\"areas_of_expertise_description\":\"Offensive security for agents, built for CI — not a runtime guardrail, not a generic code scanner.\",\"areas_of_expertise_list\":[{\"title\":\"Agentic attack library\",\"description\":\"Attacks mapped to the OWASP Top 10 for Agentic Applications and MITRE ATLAS, run against an isolated copy of the target — never your live agent.\"},{\"title\":\"Toxic-flow graph\",\"description\":\"A graph over the agent's declared tools that surfaces lethal-trifecta and untrusted-content → code-execution composition paths that single-prompt scanners structurally cannot see.\"},{\"title\":\"Side-effect divergence detection\",\"description\":\"Catches the most dangerous failure mode: the agent refuses in text while a side-effecting tool fires anyway — verified at the execution layer, with stability re-runs.\"},{\"title\":\"Capability manifest\",\"description\":\"One JSON document declares the agent's tools and their side-effect classes, data scopes, untrusted-ingress points, identity and mitigations — the contract every attack and graph is derived from.\"},{\"title\":\"SARIF for CI\",\"description\":\"Findings emit as SARIF 2.1.0 for code-scanning dashboards plus a Markdown report, and a failing verdict exits with code 1 — red-teaming becomes a CI gate, not a yearly audit.\"},{\"title\":\"Evidence without payloads\",\"description\":\"Every finding references its attack inputs by sha256 — reproducible evidence with no raw attack payloads written to disk.\"}],\"faq\":[{\"title\":\"Is it a runtime guardrail?\",\"description\":\"No. It's pre-deployment offensive testing — a red team in your CI — not a runtime filter and not a generic code scanner. Runtime guardrails are a different layer; AAL tells you where you actually need them.\"},{\"title\":\"How do I run it?\",\"description\":\"It's published on npm — npx agent-assurance scan your-manifest.json --sarif out.sarif — no install required. A bundled, deliberately vulnerable fixture agent lets you watch a full scan in one command.\"},{\"title\":\"What license is it under?\",\"description\":\"MIT, on GitHub (Moai-Team-LLC/AgenticAssurance). Free to use, self-host and build on, including commercially.\"},{\"title\":\"What does the attack library cover?\",\"description\":\"The taxonomy, schema and toxic-flow graph cover the full OWASP ASI01–ASI10 range; the seed attack corpus currently exercises ASI01, ASI02, ASI03 and ASI05, and is growing.\"},{\"title\":\"How does it relate to the rest of the ecosystem?\",\"description\":\"It's the assurance plane: it red-teams any agent in the run → remember → measure → heal loop, whatever framework it's built on — and the whole AgenticProduct stack is designed to pass it.\"}],\"get_in_touch_description\":\"Want to know how your agent holds up against the OWASP Agentic Top 10? Tell us about your product and we'll set up a red-team scan.\"},\"agentic-ecosystem\":{\"banner_title\":\"The AgenticProduct Ecosystem\",\"banner_description\":\"One open standard and six open-source reference implementations that close the loop every production agent needs: run → remember → measure → heal — with a governed model plane and a security assurance layer across all of it. Authored, maintained and dogfooded by Moai Team.\",\"banner_alt\":\"Moai Team, the AgenticProduct ecosystem of open-source agentic infrastru
264cture\",\"banner_button_text\":\"Schedule a free consultation\",\"links_path_1\":\"Agentic Software\",\"links_path_2\":\"Ecosystem\",\"what_is_title\":\"What is the AgenticProduct ecosystem?\",\"what_is_description\":\"Most teams ship agent demos; few ship agents that survive production. The AgenticProduct ecosystem is our answer to that gap: the Agentic Product Standard defines the contract — six principles, the autonomy ladder, the harness layers and eval discipline — and six open-source products implement its surfaces so you don't have to build them from scratch. AgenticOps runs the fleet; AgenticMind gives agents auditable knowledge \u0026 memory; AgenticPerformance measures every run with traces and evals; AgenticSelfHealingCode repairs what breaks; AgenticGateway is the model \u0026 cost plane every LLM call passes through; and AgenticAssurance red-teams any agent in the loop. Each product runs standalone and integrates with its siblings through optional adapters — never hard dependencies — and the whole family comes up locally with one command.\",\"areas_of_expertise_description\":\"Seven members, one loop: run → remember → measure → heal — with the model plane and the assurance plane across all of it.\",\"areas_of_expertise_list\":[{\"title\":\"Agentic Product Standard — the contract\",\"description\":\"The canonical open standard: six principles, the autonomy ladder, composition patterns, the harness layers, eval discipline and a self-assessment scorecard every member of the family conforms to.\"},{\"title\":\"AgenticOps — run\",\"description\":\"The runtime \u0026 operations layer: agents as versioned manifests, coordinated scheduling without double-runs, a durable backlog, bounded execution and fleet observability.\"},{\"title\":\"AgenticMind — remember\",\"description\":\"The knowledge \u0026 memory layer: auditable, citation-enforced answers with a replayable why-trace and a self-improving corpus, served over MCP.\"},{\"title\":\"AgenticPerformance — measure\",\"description\":\"The evals \u0026 observability layer: OTel traces, per-agent golden-set evals with a CI gate, named failure clusters and a governed improvement loop.\"},{\"title\":\"AgenticSelfHealingCode — heal\",\"description\":\"The self-healing ops layer: grounded incident diagnosis, signed signal intake and repair actions on earned autonomy, with a kill switch.\"},{\"title\":\"AgenticGateway — the model plane\",\"description\":\"The model \u0026 cost plane: one OpenAI-compatible key for the fleet, eval-measured routing, fail-closed cost ceilings and an evidence trail.\"},{\"title\":\"AgenticAssurance — the assurance plane\",\"description\":\"The security layer: red-teams any agent against the OWASP Agentic Top 10 and MITRE ATLAS, finds toxic tool-flow paths and gates CI with SARIF output.\"}],\"faq\":[{\"title\":\"Do I have to adopt all seven?\",\"description\":\"No. Every product runs standalone and earns its place on its own; integrations between siblings are optional adapters, never hard dependencies. Start with the surface that hurts most — operations, memory, measurement or security.\"},{\"title\":\"Is it really open source?\",\"description\":\"Yes — every repository is public under Moai-Team-LLC on GitHub, licensed Apache 2.0 or MIT. Free to use, self-host and build on, including commercially.\"},{\"title\":\"How do the products compose?\",\"description\":\"AgenticOps runs the fleet, AgenticMind remembers, AgenticPerformance measures, AgenticSelfHealingCode heals. AgenticGateway is the model plane every LLM call in that loop passes through, and AgenticAssurance red-teams the whole thing — all of it conforming to the Standard.\"},{\"title\":\"How do I get started?\",\"description\":\"Read the Standard and score your product against its scorecard — or bring the whole family up locally with the one-command family script from the standard repository, then adopt the layers that close your biggest gaps.\"},{\"title\":\"Can Moai Team build on it for us?\",\"description\":\"That's exactly what our agentic software development service is: we design and ship production-grade agentic products on this stack — architecture, harness, tools, memory and evals — and you keep an open-source foundation with no lock-in.\"}],\"get_in_touch_description\":\"Want a production-grade agent stack without betting on a black box? Tell us where you are and we'll map the ecosystem onto your product.\"},\"chatWidget\":{\"title\":\"Moai Assistant\",\"welcome\":\"Hi! Ask me anything about Moai Team.\",\"inputPlaceholder\":\"Type a message…\",\"send\":\"Send\",\"leaveYourContact\":\"Leave your contact\",\"namePlaceholder\":\"What is your name?\",\"emailPlaceholder\":\"Please, leave your email.\",\"notNow\":\"Not now\",\"leaveC
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