The AI Agents Production Playbook: From Demo to Dependable

Why agent demos break

An agent that works once in a screen recording is a prototype, not a product. The gap between the two is almost never model quality — it is state management, tool reliability and the absence of an evaluation loop that catches regressions before customers do.

Treat the agent as a distributed system with a probabilistic component in the middle. Everything you already know about retries, idempotency and observability applies, and applies harder.

A reference architecture

Separate planning from execution. A planner proposes steps; a deterministic executor validates each tool call against a schema before it runs. That single boundary removes an entire class of failure where the model invents arguments that never existed.

Persist every step. When an agent run takes ninety seconds and touches six systems, the trace is the only artifact that makes debugging possible.

Evaluate continuously

Build a golden set of one hundred real tasks and score every deployment against it. Automated scoring plus a weekly human review catches drift far earlier than customer tickets.

Ship behind a feature flag, expand to five percent of traffic, then widen only when the eval curve holds flat for a week.