The Engineering Boundaries That Make AI Agents Controllable
The difference between a demo and a production agent is the engineering that enforces boundaries. Without explicit state management, tool contracts, and trajectory evaluation, an agent will hallucinate actions in high-stakes domains like contracts or finance, causing irreversible damage.
Building a reliable AI agent for business tasks like contract review requires far more than connecting a model to a few APIs. The core challenge is controllability, which is solved by external engineering, not the model itself. A complete agent architecture spans intent recognition with confidence-based routing, a persistent state machine with checkpoints for long-running tasks, and a tool system where every function is a strict contract with risk ratings, idempotency keys, and structured error handling.
Structured output demands a robust pipeline that goes beyond JSON parsing to include semantic validation and safe repair strategies. Retrieval-augmented generation (RAG) must be treated as a multi-stage process of recall, reranking, and context assembly to prevent evidence loss or hallucination. Memory is split into four distinct layers—working, summary, semantic, and structured—each with its own storage and lifecycle to avoid pollution and conflicts.
Evaluation and observability form the final critical layer. Success isn't just a correct final answer; the entire execution trajectory must be audited for safety, efficiency, and budget adherence. A complete trace, from tool selection to every retry, is the only way to debug failures and prove the system is production-ready.
Treating the LLM as the brain and the surrounding engineering as the autonomic nervous system is a useful model; the brain decides, but the nervous system keeps the organism from harming itself.
The insistence on idempotency for all side-effect tools highlights a key difference from coding agents: re-running a compiler is safe, but re-sending an email or refund is a catastrophe.
Risk-rating tools into five levels (from pure reasoning to forbidden actions) creates a clear, auditable policy that prevents the model from ever being in a position to make a catastrophic decision on its own.
Structured output repair is a double-edged sword; the article rightly warns against guessing truncated enums or contradictory fields, as silent data corruption is worse than a hard failure.
The four-layer memory model solves a common anti-pattern where teams dump everything into a single vector store, mixing transient state with long-term facts and creating unmanageable recall noise.