A useful regression suite for an AI coding agent checks more than whether its final answer looks right. It tests representative coding tasks, verifies observable outcomes, inspects tool use when the route matters, and compares repeat runs without overlooking cost or safety. These five lessons offer a practical way to build that suite without treating prompt changes as harmless until proven otherwise.
1. Test the agent system, not just the prompt text
A coding agent can read files, call tools, observe results, and try again. Two runs may produce similar final answers while taking materially different routes. If the route is part of the behavior you need, grade it too.
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For each task, decide whether the requirement concerns only the outcome or also the process. A task might require a passing test, but it might also require the agent to run the test command, inspect a particular file, ask for approval, or hand off work as specified. Record those expectations as trace or metadata checks rather than inferring them from the final response. Promptfoo’s coding-agent evaluation guide describes treating agent evaluations like integration tests.
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If the question is whether file or tool access improves performance, run a plain-model baseline on the same task as well. That comparison helps separate the contribution of the agent runtime from the contribution of the underlying model.
2. Turn vague expectations into observable checks
“Write good code” is not a reliable pass/fail condition. Define what a successful run must demonstrate, then choose a check that can observe it.
- Use deterministic assertions for requirements such as a required file, output field, completion marker, exact constraint, or known defect being found.
- Use a rubric for semantic requirements that cannot be captured well by exact strings. Make the criteria explicit, and review grader decisions instead of treating them as ground truth.
- Keep the task bounded enough that a person can explain why a result passes or fails. Finding seeded defects is more measurable than asking whether a patch is generally “good.”
Promptfoo’s getting-started guide outlines a workflow that configures prompts, providers, test inputs, and optional assertions, then runs evaluations for inspection. A final-answer-only score cannot establish that a required tool action occurred; add a trace check when that distinction matters.
3. Build cases from real use and known failures
Start with the coding tasks the agent is expected to handle and the ways it is most likely to fail. A compact set of representative cases is more useful than a large set of vague or redundant prompts. Include cases for important constraints and known failure modes, not just routine success paths.
When a run fails, inspect its trace and feedback to decide whether the behavior deserves a permanent test. Traces can also suggest new cases. But a generated test is only a proposal: a person should verify that it is accurate, representative, and actually measures the behavior that matters. The OpenAI Cookbook’s agent-improvement-loop example makes that human-review requirement explicit.
Keep the cases, inputs, and expected behaviors together in a version-controlled dataset. There is no universal minimum suite size or guaranteed detection rate: the suite can only catch failures represented by its cases and grading criteria.
4. Make comparisons repeatable
Run the same stable cases when a prompt, model routing, or agent configuration changes. This makes comparisons interpretable: if the input or grading criteria also changed, a different score may not reveal whether the agent regressed.
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Some agent behavior varies between runs because tool choices, retries, and other intermediate decisions can differ. Repeat tasks expected to behave consistently when stability is part of the requirement, and inspect the relevant traces rather than relying on a single score. During development, avoid stale cached responses concealing the effect of a change.
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The OpenAI guide to evaluating agent workflows recommends starting with traces to debug workflow behavior, then using datasets and evaluation runs when the desired behavior is understood and repeatable comparisons are needed.
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5. Include resource use and safe execution in the evaluation
Correctness is not the only operational concern. For tasks where they matter, track cost and latency alongside task success. A prompt change that preserves correctness but makes runs unexpectedly expensive or slow may still be a regression for your use case.
Run evaluations that can write files in an isolated or disposable workspace. Set tool permissions and runtime boundaries deliberately, and make approval expectations part of the checks when relevant. Agent capabilities and safety controls depend on the runtime and provider configuration, so verify the current documentation for the setup you use.
Putting the suite together
A practical evaluation run can bring these elements together:
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- Run the candidate configuration against the same version-controlled input used for the baseline.
- Apply the right checks: deterministic assertions for observable requirements, a reviewed rubric for semantic ones, and trace checks for required actions.
- Record operational results such as cost or latency if they affect whether the task is acceptable.
- Inspect failures and proposed cases before changing the long-term suite; keep only checks that measure meaningful behavior.
Use the evaluation results to decide whether a change is acceptable for the behaviors you actually tested—not as proof that the agent cannot fail on unrepresented tasks.
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