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You can leave a focused 60-minute workshop with a first regression eval for one AI-agent task: representative cases, trace capture, suitable graders, a baseline run, and a plan to rerun it after changes. Treat the hour as a time-box for a useful first version, not a guarantee that every team can build a complete production evaluation system in 60 minutes.
What an agent eval needs to measure
An evaluation is a test: give the system an input, then apply grading logic to determine whether it succeeded. For an agent, a final answer alone is not enough evidence. A useful eval can include the interaction trace—such as tool calls and handoffs—and, when possible, a check that the intended outcome actually occurred in the environment.
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That distinction matters whenever an agent changes state. Saying a flight was booked does not prove that a reservation exists in a database. The Anthropic guide to agent evaluations uses this kind of gap to show why a fluent response should not stand in for verified task completion. Because an agent can take multiple steps and make errors that affect later steps, a single successful run is not proof of reliable behavior.
OpenAI’s documentation calls an informal approach “Vibe-based evals.” Replace the gut check with task-specific tests based on real-world cases and explicit success criteria, as outlined in OpenAI’s evaluation best practices.
How to build a first eval in a 60-minute session
This agenda is a practical workshop plan, not a duration validated by the cited sources. Keep the scope to one consequential, testable task. If setup work or data access takes longer than expected, preserve the eval artifacts and assign an owner for the remaining work rather than treating the schedule as a completion guarantee.
0–10 minutes: Choose one task and define success
Pick a recurring task with an outcome a reviewer can verify, such as escalating a support case correctly or completing a permitted state change. Write down what must happen for a pass and what counts as an unacceptable failure. Make the criteria specific to the task, not a broad judgment such as “the agent was helpful.”
For example, an escalation task might require the case to be routed to the correct queue and prohibit the agent from claiming it escalated the case if the routing did not happen. That is an example of how to make a criterion observable, not a universal rubric.
10–20 minutes: Assemble a small, representative starter set
Use a handful of historical or production examples the team is allowed to use, and add a few known edge cases that could materially change the outcome. Keep each input paired with an expected result or a rubric for judging it. This is a practical starting suggestion, not a source-backed universal sample size.
Include varied cases rather than only clean, easy examples. OpenAI recommends drawing from production and historical data as well as expert-created examples, growing the dataset over time, and checking that it represents production traffic. A set that poorly reflects actual traffic can bias the evaluation. See OpenAI’s evaluation best practices.
20–30 minutes: Capture the run, not just the answer
For each case, capture the input, relevant model and tool interactions, handoffs, guardrail events, final response, and the resulting environment state when it is available. The exact fields depend on the system and task; retain the evidence needed to understand whether the agent chose the right tool, handed off appropriately, followed instructions, and reached the expected outcome.
When a workflow behaves unexpectedly, traces help diagnose where it went wrong. OpenAI’s agent-evals guide recommends inspecting representative traces while debugging agent workflows.
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Use code for objectively verifiable constraints and outcomes, a rubric-based model grader for nuanced criteria, and human review to assess or calibrate subjective judgments. One task can combine multiple graders. Decide whether every condition is mandatory or whether partial credit is meaningful before settling on binary, weighted, or hybrid scoring.
A model grader should assess explicit criteria, not an open-ended question about whether a response “seems good.” Review a sample of its judgments against human assessment. A grader can also be too rigid: Anthropic describes an agent finding a better policy-compliant result than a static test expected. Investigate surprising failures before treating each red score as a product defect. The converse is equally important: a polished response should not pass if a required state change did not occur. The trade-offs are discussed in the Anthropic agent-evals guide.
40–50 minutes: Run the cases and establish a baseline
Run the suite, inspect failures in their traces, and classify them—for example, as a wrong tool choice, an instruction violation, or an unverified outcome. Do not rely only on one blended score: it can hide a critical failure among otherwise successful cases. Repeat trials when run-to-run variability could change your conclusion; model outputs can vary, and multiple trials can make results more consistent, as Anthropic notes in its agent-evals guide.
Save the run as a baseline that future changes can be compared against. The sources do not establish a universal pass-rate target for production agents. Set thresholds according to the task’s risk and what the team considers acceptable, and make any disqualifying failures visible rather than averaging them away.
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50–60 minutes: Assign the rerun loop
Save the cases, graders, and baseline. Decide who owns the eval and when it will rerun. The relevant changes may include prompts, models, routing, tools, or guardrails. If wiring the suite into CI will not fit in the session, record that as a next step instead of implying that evaluations are already automated.
OpenAI recommends continuous evaluation on changes, watching for new nondeterminism, and expanding the set as new cases arise. A first eval becomes useful over time only if the team maintains that loop; see OpenAI’s evaluation best practices.
Which graders should you use?
Choose a grader based on what needs to be established. These methods answer different questions; a mixed approach is often more informative than asking one grader to judge everything.
| Grader | Best suited to | Watch for |
|---|---|---|
| Code-based | Exact constraints, structured outputs, static analysis, and checks of environment state or other outcomes. | It is reproducible when the condition is truly objective, but a static expectation can reject a valid alternative. Inspect unexpected failures. |
| Model-based | Open-ended or nuanced rubric criteria, such as instruction following. | Use explicit criteria and compare a sample of judgments with human review. Do not treat a model-judge score as proof that an external outcome occurred. |
| Human | Expert judgments and calibration of automated graders. | Review takes more time and is more expensive to apply at scale. |
| Combined scoring | Tasks with both mandatory checks and criteria that allow trade-offs. | Use binary scoring if every requirement must pass, weighted scoring when trade-offs are acceptable, or a hybrid when some checks are mandatory and others allow partial credit. |
What should you track?
Measure the task, not just what is easy to count. Depending on the workflow, useful measures include:
- Task pass or success rate.
- Critical-failure rate, including policy or instruction violations.
- Correct tool selection, when the task depends on tools.
- Verified outcome: whether the expected change or result occurred.
- Failure categories that help the team locate recurring problems.
Use traces to understand how the agent reached its result, and outcome checks to establish whether the intended state was reached. Latency, token usage, cost per task, and error rates can matter to a decision too, but they should not replace task-success measures. Anthropic describes these operational measures as possible eval-suite metrics, while OpenAI cautions against relying only on generic metrics: Anthropic’s guide and OpenAI’s evaluation best practices.
When comparing two approaches, look at whether each can verify the outcome, how well its cases represent real traffic and edge cases, the effort and speed required for enough trials, how readily traces explain failures, whether human reviewers agree with model graders, and whether the suite can be rerun after relevant changes. These are practical comparison criteria drawn from the guidance, not a vendor benchmark.
How does the eval become part of production work?
A vendor-neutral progression is to inspect representative traces, turn recurring examples into a dataset with graders, compare prompt or workflow changes through repeatable runs, and run the suite continuously while adding newly observed failure cases. OpenAI’s agent-evals guide describes traces as a starting point for debugging and datasets with eval runs as a way to make comparisons repeatable.
OpenAI’s account of its in-house data agent offers one concrete pattern: curated question-and-answer pairs, a manually written expected SQL query, execution of the generated query, and comparison of both the SQL and resulting data. The article says those evals run continuously during development as regression checks. It is an example architecture, not a requirement for every agent: Inside OpenAI’s in-house data agent.
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Anthropic’s agent-evals guide identifies LangSmith for tracing, offline and online evaluations, and dataset management, and Langfuse as a self-hosted, open-source alternative relevant to data-residency use cases. These are examples rather than endorsements. Check each provider’s current capabilities, security terms, and availability against your requirements before choosing a tool: Anthropic’s agent-evals guide.
OpenAI Evals platform dates
As of October 5, 2026, OpenAI’s documentation says existing Evals become read-only on October 31, 2026, and the platform is scheduled to shut down on November 30, 2026. It points readers to Datasets as a more iterative starting point. Product transition dates can change, so check the current OpenAI Evals documentation before making a migration decision.
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