Evaluate before you automate.

A prototype answers a few questions well, a workflow completes successfully, and automation starts to feel like the obvious next step. This is a good moment to slow down just enough to define what “works” means.

AI systems can fail in ways that aren't visible in a quick demonstration. A response may sound convincing while using an outdated source. A document may be parsed correctly except for the one field that drives the next action. An agent may choose a valid tool at the wrong time.

Evaluation gives these possibilities a place in the development process before they become operational surprises.

Write the quality criteria first.

Start with the task, not a general score for intelligence. What must be true for the result to be usable? What would make it harmful, misleading, or simply a waste of the reviewer's time?

For a support assistant, the criteria might include using the correct policy, answering the actual question, avoiding unsupported promises, and identifying requests that need escalation. For document extraction, they might include exact field matches, handling missing values, and preserving the original evidence.

Write the criteria in language that subject-matter experts can use. An evaluation becomes more useful when the people who understand the work can explain why an output passed or failed.

Build a representative set of tasks.

Include common examples, but make room for ambiguity, incomplete information, conflicting sources, and cases outside the intended scope. These are often where the system's boundaries become visible.

Keep a stable set for comparison and a separate set that grows as new failures appear. Record the expected behavior, the supporting information, and any acceptable variation. Avoid evaluating only on the examples used to tune the system.

The dataset does not need to be enormous to be useful at the start. It needs to represent the decisions that matter and be honest about what it doesn't cover.

Look at the steps, not only the answer.

An end-to-end result is important, but it can hide the cause of a failure. Evaluate whether the system retrieved the right evidence, selected the right tool, passed valid inputs, and respected the rules around execution.

If a workflow has several steps, trace each one. Record relevant inputs and outputs, tool errors, model behavior, and timing in a way that supports debugging while respecting your data handling requirements.

This helps you make targeted changes. A retrieval issue calls for different work than a missing tool permission or an unclear instruction. Better visibility prevents endless prompt changes that leave the real problem untouched.

Combine automated checks with human review.

Some properties are straightforward to check automatically: required fields, permitted tool names, valid JSON, matching source references, or whether an action exceeded a defined limit.

Other judgments require context. Is the answer helpful? Did it preserve the meaning of a document? Is the next action appropriate for this situation? Human review is especially useful here. Model-based grading may support the process, but its judgments also need calibration against examples and expert review.

Use more than one view of quality. A single aggregate score can hide a serious failure in a smaller category of tasks. Look at important slices of the evaluation set and the actual examples behind the numbers.

Design the handoff before removing it.

A system needs an explicit way to stop, ask a question, or hand work to a person. Decide what triggers that handoff and what context the person receives. The reviewer should understand what was attempted, what evidence was used, and why the system needs help.

Consequential actions deserve particular attention. A generated draft, a proposed record change, and an executed payment are different levels of responsibility. Evaluation results should inform which steps can be automated and which require approval.

Start with the degree of autonomy your evidence supports. Expand it only when you understand the remaining failure modes and have the operational controls to manage them.

Keep evaluating after release.

Production introduces questions and conditions that a prototype never saw. Use feedback and reviewed failures to improve your evaluation set. Rerun it when models, prompts, retrieval, tools, or source collections change.

Quality is an ongoing practice. Define who owns it, what gets reviewed, and how problems lead to improvements. That practice turns a successful demonstration into a system your team can operate with clearer expectations.