A 5-layer architecture for production AI agents built around failure detection
MaryamMiradi · x · 2026-09-09
AI engineer Maryam Miradi outlines a production agent architecture premised on one idea: agents will fail, so the real engineering question is whether your system can detect, explain, recover from, and prevent failures. Her stack: (1) pick one primary agent framework before adding evals/tracing/recovery; define typed state, tool schemas, agent boundaries and MCP interfaces up front; keep the model swappable — e.g. Claude Code for implementation, LangGraph/Pydantic AI/Google ADK as runtime, MCP for tool access; (2) define success before tuning prompts or models, testing both final answers and agentic behavior. Later layers (tracing, recovery) are truncated in the excerpt.
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