Build the test set around the support work your AI agent is actually expected to do. Combine reviewed support cases with expert-written examples, cover successful resolutions as well as clarification, refusal and handoff scenarios, and record what a good result looks like for each case. There is no evidence-backed universal number of cases or coverage percentage; usefulness depends on whether the set represents your agent’s workflows and risks.
1. Define what the agent is responsible for
Start by writing down the system boundary: which customer intents the agent supports, which actions it may take, which tools it can call, and when it must refuse or hand off to a person. Test the behavior the product promises—not generic conversational ability. OpenAI’s evaluation best practices recommend evaluating typical, edge and adversarial cases; its agent-evaluation guidance addresses workflows and traces.
For each supported intent, identify the acceptable outcome. A billing question might require a direct answer, a clarifying question if the account is unclear, or escalation if the request falls outside the agent’s authority. The correct outcome depends on your policies and implementation.
2. Build the set from real cases and expert examples
Use both reviewed production or historical support cases and cases written by people who understand the product and policies. Real examples reveal how customers actually describe problems; expert-authored examples can deliberately cover uncommon but important conditions that may be missing from past logs. OpenAI’s dataset guidance describes organizing examples into reusable datasets, while its evals documentation covers evaluation datasets and ground-truth labels.
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Before using customer conversations, review and label them, and retain enough relevant history to judge the response. Remove or protect personal information according to your organization’s privacy and data-handling requirements. Do not treat an old agent reply as ground truth simply because it resolved the case: an expert should verify that the expected outcome still matches current policy.
3. Organize cases by outcome and behavior
Stratify the examples by supported intent and workflow, then include the different behaviors a capable agent may need within each one. A set made only of straightforward resolved tickets can make an agent look reliable while missing whether it knows when to pause, recover, or escalate.
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- Resolve: The agent has enough reliable information and authority to complete the request.
- Clarify: The customer’s request is underspecified, conflicting, or missing information needed for a safe answer or action.
- Refuse or limit: The request conflicts with applicable policy, permissions, or the agent’s defined scope.
- Recover: A tool fails or returns incomplete, ambiguous, or unexpected information, and the agent responds appropriately.
- Hand off: The issue requires a human or another workflow, and the agent transfers it with appropriate context.
Use only the behaviors that apply to the deployed system. OpenAI’s agent workflow evaluation guidance supports examining behavior across traces, including tool use and handoffs, rather than judging only the final text.
4. Add realistic variation and failure probes
For every important intent, vary how the issue arrives and what the agent must do with it. OpenAI’s evaluation best practices call for typical, edge and adversarial examples. The dimensions below are prompts for coverage, not quotas:
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| Dimension | Examples to include |
|---|---|
| Intent and outcome | Different supported issue types, with examples requiring resolution, clarification, escalation or refusal where applicable. |
| Input variation | Typos, alternate formats, multilingual messages, short or underspecified requests, and multiple requests in one message. |
| Conversation context | Long histories, follow-up corrections, irrelevant details, or contradictory information. |
| Tools and workflow | Correct tool choice and arguments, ambiguous results, unusual returned fields, tool errors, and appropriate handoffs. |
| Policy and instructions | Requests that conflict with system instructions, attempts to override them, and required response formats. |
| Evidence and grounding | When the agent uses documents, cases that test whether its claims are supported and whether it represents the source accurately. |
Prioritize dimensions according to the tasks and consequences in your own support operation. A low-impact wording variation does not necessarily deserve the same attention as a tool action that could change an account or a policy-sensitive request.
5. Record expected results and grading criteria
Keep a consistent record for each case so it can be reviewed and run again. A practical case can include:
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- Customer message and relevant conversation history.
- Relevant tool inputs and outputs, if the case tests tool use.
- Expected outcome or acceptable response properties, plus human labels or a reference answer where useful.
- Criteria for judging correctness, policy compliance, and any required workflow behavior.
Grade the user-visible answer against criteria specific to the task. If the agent’s process matters, evaluate the trace as well: Did it choose the right tool and arguments? Did it follow instructions, handle an error, or hand off when needed? A correct-sounding final reply does not by itself show that the agent followed the required workflow.
For document-grounded answers, check whether cited evidence supports the claims, whether the response captures the source’s full message, and whether the evidence is sufficient for the strength of the claim. NIST identifies faithfulness, completeness and sufficiency as evaluation-probe concerns in Building Evaluation Probes into Agentic AI.
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Human or expert review is especially useful for judging whether examples are realistic and whether ambiguous cases have sensible expected outcomes. Automated graders can make repeated evaluation more practical, but their criteria and judgments also need review; they should not be treated as authoritative labels for every support case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Keep the set stable enough to compare and flexible enough to grow
Preserve a stable core of cases so you can compare behavior across meaningful changes to prompts, models, tools, routing, or workflows. Run the same cases after those changes to identify regressions. Add new examples when monitoring, support review, or a system change reveals a failure mode or blind spot. OpenAI’s dataset guidance recommends expanding datasets as new edge cases are identified, and its agent evaluation guidance describes repeatable evaluation runs for comparing changes.
When reviewing whether a set is representative, look at breadth across intents and workflows, realism of language and context, policy and adversarial coverage, and the tools and handoffs the agent actually uses. The appropriate balance depends on the product; the available guidance does not prescribe a universal weighting.
How many test cases do you need?
There is no universal sample count, sampling ratio, or minimum coverage percentage established for AI customer-support agents. A raw total can hide gaps: many near-identical password-reset examples do not establish that a set covers tool failures, ambiguous requests, or required escalation. Judge the set by whether it exercises the supported workflows and important failure modes, then expand it as evidence from use and review exposes omissions.
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