Research Insights Made Simple 26How to Build Working Evals for AI Agents (Agents column)

How do you know if an AI agent is ready for production? A beautiful response and even a high "pass rate" show only the final of a single launch. The agent could peek at the solution, choose a dangerous path, violate rights or crumble when rerun.

Friday, 31 July, in 16:00 MSC will pass live "Research Insights Made Simple" together with Evgeny Sergeev - Engineering Director at Flow Health with twenty years of experience in developing and managing engineering teams. We will discuss how to turn evals from a one-time response check into a reproducible engineering system.

The minimum unit of such a system is a repeatable episode. (replayable episode)Frozen initial state, input data, agent contract, covert verification, course of action and release criterion. For code, this can be commit to PR and hidden testsFor architecture, requirements, constraints, and executability testing; for data platform, snapshot data, lineage, and invariants.

Let's talk:

Why you should evaluate the entire agent system, not just the model How to freeze the initial state and prevent the agent from looking at the future solution What to fix in the contract: tools, rights, network, time and budget; Why check not only the result, but also the trajectory of actions? Why one successful launch is not enough and repeat runs are necessary

• How to collect production scorecard Outcome, trajectory, cost, safety and human acceptance How to connect offline-evals with real production outcomes and turn them into release gates.

For me, the big takeaway is that sustainable quality doesn't come from one metric or one model. You need your own catalog of real tasks, reproducible checks and a clear criterion, after which the new version of the agent can really trust the work.

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