To catch multi-turn AI agent regressions before release, replay a versioned set of realistic tasks in a controlled environment, grade both task outcomes and important interaction behaviors, and inspect the full trace—not just the final reply. Run the suite when relevant prompts, models, tools, routing, or agent code change. Keep monitoring production too: no finite offline test set can anticipate every conversation.
What conversation regression testing measures
An evaluation combines a test input with grading logic. For an agent, the input may be a complete task with prior conversation turns, available tools, and an environment—not merely one prompt paired with one answer. Errors can compound across turns, so the record should preserve the conversation, tool calls and responses, intermediate results, and final state when available. See Anthropic’s guide to agent evaluations.
Regression testing asks whether tasks the agent previously handled still work after a change. Capability evaluation asks what the agent can do or learn to do better. Keep those questions separate: a low capability score is not automatically a regression, and a green regression suite does not prove broad competence.
Build a useful test case from a real task
Start with a user task that matters to the product, then specify enough context for another run to be interpretable. A case should include:
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- Request and context: the initial user message and any relevant prior turns, including constraints or facts the agent must retain.
- Tools and environment: which tools are available and the controlled state in which the agent runs.
- Success criteria: observable outcomes and required behaviors that genuinely follow from the task.
- Inspection method: how to check the final answer, relevant trace details, and resulting environment state.
- Run configuration: the model, prompt or instructions, tools, routing, and other agent settings needed to make comparisons meaningful.
Useful cases come from product requirements, carefully curated production failures, and edge cases. If production conversations are used, remove or protect sensitive data according to the team’s data-handling policy. Store each case with its intended behavior, context, graders, and relevant configuration so that a changed score can be explained. There is no single storage schema established by the evaluation guidance.
Make the task and grader agree. If the task says to update a record, the grader should verify the update—not just award credit for saying it was done. Ambiguous instructions can make an agent appear to fail even when the intended behavior was unclear.
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Grade outcomes, traces, and conversation quality
Use several kinds of checks where needed; completion, interaction quality, and safety are different properties. Deterministic assertions are a good fit for facts that can be verified directly, while rubric grading is useful for qualities that cannot be reduced to a simple assertion.
| What to evaluate | Example check | Best fit |
|---|---|---|
| Task outcome | Did the intended record, setting, or other environment state change? | Deterministic end-state or functional assertion |
| Instruction following and context | Did the agent respect constraints and use information from prior turns? | Assertions for specific requirements; rubric for nuanced judgment |
| Tool use | Was the appropriate tool selected, and were its arguments correct? | Trace inspection and checks on consequential tool calls or arguments |
| Handoff | When escalation or transfer was required, did the agent hand off appropriately? | Assertion or rubric tied to the case’s requirements |
| Interaction quality | Was the exchange clear, appropriate, and aligned with the user’s intent? | Rubric grader calibrated against human judgments |
A final message is not proof that the requested action happened. Conversely, requiring one exact sequence of tool calls can reject a valid alternative route to the same correct result. Test a specific trajectory only when that sequence matters for correctness or safety; otherwise grade the outcome and decision quality, and allow partial credit if the case warrants it.
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For long flows, evaluate the conversation thread: whether the agent understood the user’s intent, completed the task, and took an acceptable path. One practical pattern is N-1 testing: provide the agent with the first N-1 turns of a real conversation and ask it to produce the final turn. For interactive flows, a conditional test can check each turn and continue only when that turn meets the case’s expectation. These approaches avoid assuming every conversation follows one rigid script. LangChain discusses run-, trace-, and thread-level evaluation in its evaluation resource.
Run the suite throughout development
- Create a small, high-value baseline. Include known tasks and durable failure cases before expanding into low-impact variations.
- Run it on relevant changes. Trigger checks when prompts, models, tools, routing, or agent code change. OpenAI’s agent evaluation guide describes traces, graders, datasets, and evaluation runs.
- Repeat trials in proportion to risk. Model behavior can vary between runs. Choose the number of trials based on the task’s risk, runtime, and cost; there is no universal trial count that fits every agent.
- Review failures by layer. Determine whether a problem came from the final response, tool choice or arguments, a handoff, instruction following, or the resulting environment state.
- Add durable cases, not brittle noise. Turn a new failure into a regression case when it represents a lasting, user-relevant requirement. Avoid making every harmless wording variation a permanent assertion.
Keep offline regression tests and production monitoring in the same feedback loop. Offline cases have known inputs and clearer references; live evaluation can reveal unexpected requests and gradual degradation. Use those discoveries to improve the suite, while continuing to monitor for cases the suite does not cover.
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Choose an evaluation approach that fits the agent
Compare approaches on the work your team needs to do, rather than assuming one platform or level of evaluation is best for every system.
- Evaluation unit: Can you test an individual decision, a complete trace, or a whole conversation thread?
- Evidence captured: Are tool calls, intermediate results, and environment state available for inspection?
- Test management: Can the workflow maintain datasets, run repeated trials, apply multiple graders, and compare regressions?
- Trajectory flexibility: Can graders accept more than one valid route to a correct outcome?
- Integration and operations: Does the approach fit the agent framework and CI workflow, and does it support the monitoring needed in production?
- Maintenance burden: Can the team keep cases, graders, and expected outcomes accurate as the agent changes?
Official examples include OpenAI’s agent evaluation documentation, LangChain’s evaluation resource, and Promptfoo’s agent integration guides for systems including CrewAI and LangGraph. Treat these as starting points for checking fit with your stack, not as evidence that one vendor is universally superior.
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