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A prompt regression suite helps you detect whether a prompt or agent-configuration change has altered coding behavior you care about. The useful test is not only whether the final answer looks right: it may also need to check code correctness, tool use, safety, repeatability, cost, or latency. The five lessons below are practical guidance, not a first-person account of an undocumented implementation.
1. Test the agent system, not just the final text
A coding agent can read files, run commands, observe results, and revise its work. Two runs may produce similar final explanations while taking materially different paths. If a required action matters, make it part of the evaluation.
For example, if the task requires the agent to run tests, check the execution trace or available run metadata for that action rather than inferring it from the final response. Likewise, assert that it read relevant files, invoked the expected tool, requested approval when needed, or followed a handoff. The appropriate checks depend on what the agent runtime exposes.
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2. Turn expectations into observable checks
Translate each important expectation into a check that can be judged consistently. Use exact assertions for requirements with crisp boundaries; use a rubric for semantic qualities that cannot be captured reliably by literal matching.
- Exact checks: required files exist, output fields are present, a completion marker appears, or a known seeded defect is found.
- Rubric checks: a change preserves intended behavior, follows a policy, or gives an adequate explanation. Review grader decisions rather than treating a model-based score as ground truth.
- Trace checks: a particular tool action or workflow step occurred when the route itself matters.
Choose tasks with outcomes that can be explained and graded. Promptfoo recommends core use cases and likely failure modes as a starting point, and its guide contrasts subjective judgments with measurable checks such as finding intentional bugs: Promptfoo’s getting-started guide and coding-agent evaluation guide.
3. Build cases from representative tasks and known failures
Start with the coding tasks your agent is meant to handle and the ways those tasks are likely to go wrong. A useful case has a clear input, an expected behavior, and a reason that behavior matters. Include observed failures as they arise so a fix can be checked against them in future runs.
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Traces and user feedback can suggest new cases, but they do not automatically make good tests. Before keeping a proposed case, have a person check that it is accurate, representative of real work, and measuring the intended behavior. OpenAI’s agent-improvement example explicitly recommends human review before generated evaluations become part of a long-term suite: Build an Agent Improvement Loop with Traces, Evals, and Codex.
Keep the cases, inputs, and expected behaviors in a version-controlled dataset. The suite then becomes a durable comparison point as prompts, models, or routing change. No universal minimum suite size or guaranteed detection rate is established by these guides; coverage depends on the cases and grading criteria you choose.
4. Make repeatability part of the design
Run the same stable cases when changing prompts or agent routing, and repeat cases whose behavior is expected to be consistent. Agent decisions and retries can vary, so one successful run is not always enough to establish stability.
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During development, ensure cached responses are not masking a change. Inspect traces while diagnosing workflow behavior; once the desired behavior is understood and repeatable, use datasets and evaluation runs for structured comparisons. OpenAI describes trace grading as a fast way to identify workflow-level issues and recommends datasets and eval runs for repeatable comparisons: Evaluate agent workflows.
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5. Track operational cost and protect the workspace
Success is not the only meaningful result. For tasks where resource use or responsiveness matters, record cost and latency alongside correctness. Choose thresholds for your own workload rather than treating illustrative configuration values as universal benchmarks.
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Because coding-agent evaluations may write files or invoke tools, run them in an isolated or disposable workspace. Be explicit about the available permissions and runtime boundaries, and verify the current provider and tool configuration. A test should not have broader access than it needs.
What to compare between prompt versions
Choose comparison measures based on the behavior you intend to preserve or improve:
- Task success and correctness against a measurable expectation.
- Instruction and policy adherence.
- Tool choice and execution path when a specific action is required.
- Structured-output validity when another system consumes the result.
- Cost and latency when resource use matters.
- Stability across repeated runs when consistent behavior is expected.
- Performance against a plain-model baseline when evaluating the value of agent tools or file access.
A final-answer-only score cannot show that a required tool path occurred. Conversely, trace checks are unnecessary when intermediate actions do not affect the behavior you need to evaluate.
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