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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →A representative test set for an AI customer-support agent should reflect the real support workflows the agent is expected to handle—not just a collection of common questions. Combine reviewed support cases with expert-written examples, cover typical, edge, and adversarial situations, and define what a successful answer or action looks like for each case. Include tool use and handoffs when they are part of the deployed workflow, then rerun stable cases after meaningful system changes.
Start by defining what the agent is responsible for
Before collecting examples, write down the boundary of the system being evaluated. List the support intents it handles, the actions it may take, the cases it must refuse or escalate, and the tools or human handoffs available to it. This keeps the evaluation focused on the product’s actual promise rather than generic conversational ability. OpenAI’s evaluation best practices and agent-evaluation guidance both emphasize evaluating behavior in context.
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For each intent, describe the acceptable outcomes. A billing question might require a direct answer when the account information is available, a clarifying question when it is not, or a handoff when the agent lacks authority. The exact outcome depends on the deployed system; the important point is to make it explicit before grading.
Build the set from real cases and expert-written examples
Use a mixture of historical or production support cases and cases written by people who understand the support policies and workflows. Real examples preserve the language, context, and messiness of customer interactions. Expert-authored cases let you deliberately test situations that may be rare in logs but important to handle correctly.
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Review real cases before using them. Keep the conversation context needed to judge the agent’s response, and label each example with its expected outcome or acceptable response properties. OpenAI’s dataset guidance describes structured test items and human-provided ground truth as foundations for repeatable evaluation.
Cover behavior, not just support topics
A list of intents alone can make a test set look broad while missing important ways the agent can fail. For each supported workflow, include examples that test whether the agent should resolve the issue, ask for clarification, refuse, recover from a tool problem, or hand off to a person. Include only behaviors relevant to the system’s actual permissions and design.
| Coverage dimension | Examples to include |
|---|---|
| Support intent and outcome | Each supported issue type, with resolution, clarification, escalation, or refusal as appropriate. |
| Input variation | Different languages, typos, alternate formats, underspecified requests, and multiple requests in one message. |
| Conversation context | Long histories, irrelevant or contradictory details, and customer corrections in follow-up messages. |
| Tools and workflow | Correct tool selection and arguments, ambiguous results, tool errors, and appropriate handoffs. |
| Policy and instructions | Requests that conflict with system instructions, attempts to override them, and required response formats. |
| Evidence and factual grounding | When the agent uses documents, whether its claims are supported and whether it represents the source fully. |
These are prompts for finding gaps, not quotas. Allocate more attention to failures with greater consequences for the support tasks your agent performs. OpenAI’s guidance recommends typical, edge, and adversarial cases; it does not prescribe a universal distribution among them.
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Make test cases realistic and reproducible
A useful case contains enough information for another person—or an evaluation system—to judge the result consistently. Depending on the workflow, record:
- The customer’s message and relevant conversation history.
- Tool inputs and outputs available to the agent.
- The expected outcome or acceptable response properties.
- Human labels or a reference answer, where useful.
- The criteria graders should apply.
Use a stable format so the same cases can be rerun and compared. For a tool-using agent, the case should preserve the tool results and workflow context that shaped the expected behavior; otherwise, a changed response may be caused by a changed test input rather than a changed system.
Grade the answer and the workflow
Assess the user-visible result against criteria specific to the task: did the agent resolve the issue accurately, provide an appropriate explanation, or ask for information it genuinely needed? For workflows that use tools or handoffs, grade those actions too. A polished final message does not make a case successful if the agent used the wrong tool, supplied incorrect arguments, ignored an error, or failed to escalate when required.
For document-grounded answers, check whether the evidence supports the claims, whether the answer captures the source’s relevant message, and whether the evidence is sufficient for the claim. NIST identifies these as faithfulness, completeness, and sufficiency probes in its work on evaluation probes for agentic AI.
Automated graders can make repeated evaluation practical, but their criteria and judgments also need review. Use human or expert review to catch unrealistic examples, ambiguous expected outcomes, and grader mistakes. The available guidance does not establish an automated grader as an authoritative label for every customer-support case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the test set current without losing comparability
Treat the set as a maintained evaluation asset. Add cases when production monitoring, human review, or a system change exposes a blind spot. Keep a stable group of cases for comparison, and run it after meaningful changes to prompts, models, tools, or routing. OpenAI recommends expanding datasets as edge cases emerge and using repeatable datasets and evaluation runs to compare agent changes.
When assessing whether a set is representative, review its breadth across intents and workflows, realism of language and context, coverage of policy-sensitive and adversarial behavior, and treatment of tools and handoffs. There is no source-supported universal sample size, sampling ratio, or minimum coverage percentage for customer-support agent test sets. Choose the set’s size and emphasis based on the workflows and risks it needs to measure rather than treating an unsupported number as a benchmark.
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