Before you add an AI agent, define what good looks like

An AI demo only has to succeed once. A real workflow has to survive incomplete requests, contradictory data, unfamiliar formats, model updates, and a user who is already late. That difference is why a promising prototype can feel unreliable the moment it meets day-to-day work.

The instinct is often to improve the prompt or add a more capable agent. That can make the happy path look better while leaving the actual product risk unchanged. Before expanding capability, define the result the workflow is expected to produce and the mistakes it must never make.

Start with twenty to fifty examples taken from the real job. Include routine cases, ambiguous cases, missing information, and the awkward edge cases people remember because they caused rework. For each one, write down what a strong answer contains, what an acceptable answer may omit, and what should trigger escalation.

The scorecard should use observable criteria: required fields were extracted, totals reconcile, citations point to the supplied source, tone matches the brand, or the system asks for missing context instead of inventing it. “Feels smart” is not a criterion; two reviewers should be able to reach roughly the same conclusion.

The output depends on more than the model name. Instructions, retrieved documents, tool permissions, data freshness, conversation history, and interface constraints all shape the result. A model test can pass while the product fails because the wrong record was retrieved or a tool returned partial data.

Run evaluations through the same path a user will take. If the product can search, upload a document, or call an internal API, those steps belong in the test. This makes failures actionable: the team can see whether to change the prompt, the retrieval layer, the tool contract, or the experience around the answer.

A reliable AI feature knows when not to act. High-impact actions should have explicit confirmation, narrow permissions, reversible operations, and a clear handoff to a person. Low-confidence output should look different from verified output; uncertainty cannot live only in a log file.

Keep the evidence needed to understand a miss without collecting data you do not need. Record the input class, tools used, output, reviewer decision, and failure reason. Those signals turn a vague complaint—“the AI was wrong”—into a fixable product issue.

Begin with a narrow workflow where a person can review the result quickly. Compare against the current manual process, measure quality and time saved, and watch the error categories rather than one average score. A single severe miss can matter more than twenty polished drafts.

Every meaningful production failure should become a new test case. Over time, the evaluation set becomes a practical specification for the product: it preserves what already works while giving the team room to change models, tools, and prompts with confidence.