What It Actually Takes to Get AI Agents Into Production
Most AI agent pilots never ship. Here's the architecture and evaluation discipline that separates a demo from a system your team can trust.
An AI agent that works in a demo and an AI agent that survives production are two different engineering problems. The demo needs a happy path. Production needs guardrails for every way a tool call, a retrieval, or a user's phrasing can go sideways.
The teams that get past the pilot stage treat evaluation as infrastructure, not an afterthought. That means a test set of real tasks, a way to score outputs beyond 'it looked right', and a habit of re-running that evaluation every time a prompt, model, or tool definition changes.
It also means designing agents around explicit permissions and narrow tool scopes rather than broad autonomy. An agent that can only take a small set of well-defined actions is easier to trust, easier to debug, and easier to explain when something goes wrong.
None of this is exotic. It's the same discipline that makes any software system production-grade — logging, evaluation, staged rollout — applied to a component that happens to be non-deterministic.
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