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What makes an agent eval more than a final-answer review?
An evaluation is a test: give a system an input and apply grading logic to measure whether it succeeded. For an agent, the final response is only part of the evidence. A useful eval can also capture the sequence of model and tool interactions, handoffs, guardrail events, and the resulting state in the environment.
That distinction matters whenever the agent is expected to act. An agent saying it booked a flight does not prove a reservation exists in the database. Likewise, a support agent saying it escalated a case does not prove that the case reached the right queue. When possible, verify the outcome directly in the system of record, alongside any checks on the agent’s reasoning path or response. Anthropic’s guide to evaluating AI agents explains why traces and outcome checks matter for multi-step systems.
OpenAI’s evaluation guidance calls “Vibe-based evals” an anti-pattern: informal impressions are difficult to reproduce and compare. A repeatable test makes the success condition explicit, records the case and grader, and lets the team compare behavior across changes. OpenAI’s evaluation best practices recommend task-specific tests grounded in real-world data.
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How to run a 60-minute first-eval workshop
Use the hour to create a narrow, useful regression test—not to claim comprehensive coverage. The schedule below is a practical workshop plan synthesized from published evaluation guidance; it is not a validated time estimate. If your team cannot automate the rerun during the session, leave with an owner and a specific next step.
Minutes 0–10: Choose one consequential task
Pick a recurring agent task with an outcome someone can verify, such as routing a support case to the correct team or completing a permitted state change. Write down:
- the input the agent receives;
- what must be true for the task to count as successful; and
- one or more unacceptable failures, such as an unauthorized action or a case sent to the wrong queue.
Keep the first eval specific. “Handle support well” is too broad to grade consistently; “route this billing dispute to the billing queue without changing the customer’s account” gives reviewers and graders observable conditions.
Minutes 10–20: Assemble representative cases
Start with a handful of historical or production examples the team is permitted to use, then add a few edge cases that matter to the task. Preserve each input with its expected result or a grading rubric. This is a practical starting point, not a universal sample-size rule.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsExamples should reflect actual usage, not just easy demonstrations. OpenAI recommends using production and historical data as well as expert-created examples, and expanding the set over time. A dataset that does not represent production traffic can bias what the eval appears to measure.
Minutes 20–30: Capture the whole run
For each run, retain the input, model and tool interactions, handoffs, relevant guardrail events, final response, and the state needed to verify the outcome. Make sure the trace can answer practical debugging questions: Did the agent choose the right tool? Did it pass work to the right component? Did it follow the instruction? Did the intended change actually happen?
OpenAI’s agent-evals guide recommends inspecting representative traces when debugging workflow behavior. A final response alone often cannot reveal where a multi-step run went wrong.
Minutes 30–40: Add checks that match the success conditions
Use deterministic checks for facts or outcomes that can be verified exactly, such as whether a case’s queue ID matches the expected value. For nuanced criteria—such as whether the agent followed a complex instruction—use a rubric-based model grader or human review. If you use a model grader, have a person review a sample of its judgments and calibrate it before treating its scores as dependable.
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Choose scoring to fit the task. A binary pass may be appropriate when every required condition is mandatory; weighted scoring may make sense when partial credit is meaningful; a hybrid can make some checks mandatory while allowing partial credit on others.
Minutes 40–50: Run the suite and inspect failures
Run the cases, inspect the failing traces, and classify what happened instead of relying only on one blended score. If an agent’s run-to-run variability could change the conclusion, repeat trials. Anthropic notes that multiple trials can improve consistency in evaluation results because model outputs vary.
Minutes 50–60: Save the eval and name its next owner
Save the cases and grader configuration so another person can rerun them. Identify which changes should trigger a rerun—such as a prompt, model, routing, tool, or guardrail change—and assign someone to connect the suite to that change path. OpenAI recommends continuous evaluation on changes, monitoring for new nondeterminism, and growing the test set over time. If wiring the suite into CI does not fit the hour, document who will do it and when.
What should a production agent eval measure?
Choose measures that answer whether the agent completed its task safely and correctly. Depending on the workflow, useful measures include:
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- Task success or pass rate: whether each case met its defined success conditions.
- Critical failure rate: how often a disqualifying error occurred, such as an unauthorized state change.
- Verified outcome: whether the intended change or result exists in the relevant environment.
- Tool selection and handoff: whether the agent used the right tool or transferred work appropriately, when those behaviors matter.
- Instruction or policy violations: whether the run breached a constraint.
- Failure categories: what went wrong, so teams can distinguish, for example, a tool-selection error from a failed state update.
Track latency, token usage, cost per task, or error rates when they matter to a decision, but do not let convenient operational numbers stand in for task success. Anthropic describes these operational measures as possible eval-suite metrics; OpenAI cautions against relying only on generic metrics. The appropriate measures depend on the task, not on a universal agent benchmark.
Which graders should you use?
Code, model, and human graders answer different questions. A task can combine them; the important thing is to make the criteria explicit and verify subjective automated judgments.
| Grader | Best fit | Strength | Watch for |
|---|---|---|---|
| Code-based | Exact constraints, structured outputs, static analysis, or environment-state checks | Reproducible when the condition is genuinely objective | A rigid test can reject a valid solution if it encodes only one expected path or answer. |
| Model-based | Open-ended rubric criteria, such as nuanced instruction following | Can assess responses that do not have one exact string match | Judgments need explicit criteria and calibration against human review; an unbounded “does this seem good?” prompt is hard to trust. |
| Human | Expert judgment, reviewing ambiguous cases, and calibrating automated graders | Can apply context-sensitive judgment | Slower and more expensive to use at scale. |
Anthropic describes these trade-offs in its agent-evals guide. It also cautions, in effect, that a red score is a reason to investigate, not automatic proof of a product defect: a static test can reject an agent that found a better policy-compliant result than the test anticipated. But a fluent response should not pass when a required environment change did not occur.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you turn failures into a useful baseline?
A baseline is the recorded result of the eval cases under a known configuration—not a guarantee that the agent is reliable in every situation. Save which prompt, model, routing, tools, and guardrails were in use alongside the run and its traces, so later comparisons have context.
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When a case fails, inspect the trace and outcome evidence, then classify the failure before deciding what to change. This helps distinguish an agent behavior problem from an overly narrow grader, a missing example, or a tool or environment issue. If a model-based grader flags a surprising result, compare its reasoning against the rubric and a human review rather than accepting the score without inspection.
Repeat trials when variability could affect the decision. One successful run does not establish that a multi-step agent will behave reliably on the next attempt; repeated runs help show whether a result is stable enough to compare.
How should the eval fit into production development?
Use the first suite as a maintained regression check. Rerun it when relevant parts of the system change, inspect new failures, and add meaningful cases from failures observed in use. OpenAI’s guidance emphasizes continuous evaluation and growing the set over time; an eval that is never rerun cannot tell you whether a change improved or regressed the task.
A vendor-neutral progression is to inspect representative traces to understand behavior, formalize repeated examples and graders in a dataset, compare system changes with repeatable runs, and then run the suite continuously. OpenAI’s agent-evals guide describes traces as a starting point for workflow debugging and datasets plus eval runs as a way to make comparisons repeatable.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11One concrete example comes from OpenAI’s in-house data agent: its evals use curated question-and-answer pairs and manually authored expected SQL, execute the generated query, and compare both the SQL and the resulting data. The article says those evals run continuously during development as regression checks. That is one architecture for a data agent, not a requirement for every team.
Check product-transition dates before choosing a platform
OpenAI’s documentation currently says its Evals platform is being deprecated: existing evals become read-only on October 31, 2026, and the platform is scheduled to shut down on November 30, 2026. The same documentation suggests Datasets as a more iterative starting point. Because these are time-sensitive product dates, check the current OpenAI Evals documentation before making a migration decision.
Anthropic’s guide identifies LangSmith as an example of a tool offering tracing, offline and online evaluations, and dataset management, and Langfuse as a self-hosted open-source alternative with data-residency use cases. These are examples, not endorsements; confirm current features, security terms, and availability directly with the vendors before adopting a tool.
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