AI Agent Architecture Blueprint — Production-Tested Multi-Agent System Design

### Short Description A complete multi-agent architecture with Executive-to-Specialist delegation, routing matrix, governance integration, and framework-specific implementation guides for OpenAI Agents SDK, LangGraph, CrewAI, and custom builds. ### Full Description **Your agents are a mess.** You've got multiple AI agents, but no clear hierarchy. They step on each other. You can't audit who did what. Boundaries are fuzzy, delegation is ad-hoc, and the whole thing becomes unpredictable the moment you add a third agent. **This blueprint fixes that.** It's a complete, production-tested multi-agent architecture extracted from a real AI company. One Executive orchestrator. Five specialist agents. A routing matrix that maps every task type to exactly one responsible agent. Zero ambiguity. **What's Inside:** ✓ **Executive Agent spec** — The orchestrator. Breaks tasks down, delegates, enforces governance ✓ **5 Specialist Agent specs** — Validator, Audit, Docs, Research, Comms — each with role, inputs, constraints, required behavior, and output format ✓ **Agent Index with Routing Matrix** — Every task type maps to exactly one agent. No overlap, no confusion ✓ **Delegation Hierarchy** — Visual architecture diagram with clear rules (specialists never delegate to each other) ✓ **Governance Reading Order** — How agents load and follow policies before acting ✓ **Implementation Guide** — Working code examples for OpenAI Agents SDK, LangGraph, CrewAI, and custom Python/TypeScript **The Architecture:** ``` Executive Agent (Orchestrator) ├── Validator Agent — Gatekeeper for releases and submissions ├── Audit Agent — Structured logging and traceability ├── Docs Agent — Documentation within governance ├── Research Agent — External context and research └── Comms Agent — Human-facing communications ``` **Key Design Rules:** - Executive orchestrates — specialists execute - Specialists NEVER delegate to each other - Every agent reads governance before acting - Every action produces structured, auditable output **Who This Is For:** - Teams building multi-agent AI systems (OpenAI, LangGraph, CrewAI, AutoGen, custom) - Developers who need agents that are predictable, auditable, and governed - CTOs who want agent architecture that doesn't become chaos at scale **Tags:** ai-agents, multi-agent, agent-architecture, openai, langchain, crewai, developer-tools, ai-governance

Found on Devadex — the discovery index for independent software the big search engines bury. More from gumroad.

Report this listing