How We Evaluate an AI Agent Before It Touches Production

A demo where an AI agent handles three well-behaved example inputs tells you almost nothing about how it will behave on the thousandth real one. Before anything with agent-like autonomy goes anywhere near production, it needs to pass a small, deliberately boring evaluation process.

Start with a golden dataset, not a vibe check

The first thing we build isn't the agent — it's a set of real or realistic inputs with known-correct outputs, pulled from the actual problem domain, including the awkward and ambiguous cases a demo would never include. Running the agent against this set repeatably is what turns "it seemed to work" into an actual pass rate you can track across prompt changes and model version upgrades.

Tool-calling accuracy matters more than the final answer

If an agent's job involves calling tools or functions — looking something up, writing to a database, triggering an action — the accuracy of which tool it calls and with what arguments matters as much as whether the final response reads well. A wrong tool call that happens to produce a plausible-sounding final answer is a worse failure than an agent that visibly gets confused, because the wrong one is much harder to notice in production.

Decide the safety guardrails before the agent has permissions

Anything an agent can actually do — send an email, modify a record, spend money — needs an explicit boundary decided in advance: what it's allowed to do without confirmation, what it must ask a human about first, and what it should never be able to do regardless of what it's asked. Retrofitting these boundaries after an agent already has broad permissions is a much harder conversation than designing them in from the start.

What "ready" actually looks like

An agent is ready for production not when it handles the happy path well, but when its failure modes on the golden dataset are known, bounded, and acceptable — and when there's a clear answer for what happens when it's wrong. That's a lower bar than "never wrong," and a much more honest one.