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To tell whether your AI agent got better, compare the old and changed versions on the same representative tasks, grade them against explicit success criteria, and inspect what happened during each run—not just the final answers. Repeat tests when outcomes vary, and weigh task results against latency, cost, and errors. A higher score is evidence about the cases you tested, not proof of a broad or lasting improvement.
Define what “better” means for this agent
Start with the job the agent is supposed to do. Turn the intended user outcome into observable criteria before changing the agent. Depending on the task, success might require a correct result, a required tool action, safe completion, or an appropriate escalation.
Anthropic defines an evaluation as giving an AI an input and applying grading logic to its output to measure success in its engineering guide, “Demystifying evals for AI agents”. For an agent, that means testing its actual workflow and outcomes. A broad model benchmark can offer context, but it does not replace criteria tailored to the application. OpenAI’s agent evaluation guide and evaluation best practices also emphasize application-specific tests and grading.
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Collect actual or realistic tasks that reflect the agent’s intended use. For each case, record the expected outcome or a rubric explaining how to judge it. Preserve a stable subset so you can compare versions over time, and add newly observed failures or changed requirements deliberately rather than silently replacing old tests.
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OpenAI’s agent evaluation documentation describes using datasets and evaluation runs to benchmark changes. Anthropic’s guide on evaluating AI agents discusses static task banks for baselines and regression measures. Record the tested agent version and configuration alongside the cases and results so that a future comparison can be repeated.
Compare versions under the same conditions
- Save a baseline. Keep the old version identifiable, along with its configuration and results on the test set.
- Run both versions on the same cases. Use the same grading rules so a change in the test or rubric does not masquerade as an agent improvement.
- Record the change. Note what changed in the agent configuration as well as the results.
- Repeat variable runs. If the agent’s outputs or tool decisions vary, evaluate it more than once rather than treating one run as a stable score. OpenAI’s evaluation best practices recommend accounting for variability and nondeterminism.
There is no universal number of test cases or runs that proves an improvement. The appropriate amount depends on how variable the agent is, how varied its real tasks are, and how consequential its mistakes would be. A small score shift on a narrow set may reflect the particular cases sampled, not a dependable change across unseen tasks; Anthropic explains this sampling issue in “A statistical approach to model evaluations.”
Score the result, then inspect the trace
Use deterministic checks when an outcome can be verified directly, such as whether a required field is present or a calculation matches its expected result. Use a rubric or human review when quality requires judgment. For failures or changed outcomes, inspect the full run to find where behavior diverged: tool choice, tool execution, intermediate results, or the final response.
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Measure operational tradeoffs alongside success
Task success is not the only measure that may matter. Track the operational costs and reliability dimensions relevant to the application alongside outcomes:
- Latency: how long a task takes.
- Token use and cost per task: whether the improvement requires more resources.
- Error rates: whether failures became less frequent—or moved to a different part of the workflow.
Anthropic’s guide to agent evaluations lists these as measures that can be tracked on a static task bank. Decide in advance which tradeoffs are acceptable for the agent’s purpose; a gain in task completion may not be worthwhile if it comes with an unacceptable increase in time, cost, or errors.
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Offline tests make comparisons repeatable, but they cannot by themselves show that a change helps with the tasks people actually bring to the agent. Compare evaluation results with observed production outcomes and review whether real interactions show the same pattern. LangSmith documentation describes both curated offline evaluation and comparing recent production runs with actual outcomes in its page on evaluation types.
Keep the test set current. A fixed benchmark can become unrepresentative as real tasks or requirements change, and it can stop distinguishing versions once the agent reliably passes its solvable cases. Add relevant new cases and check that expected outcomes still reflect current requirements; Anthropic discusses benchmark saturation and static task banks in “Demystifying evals for AI agents.”
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What counts as convincing evidence?
The strongest practical case is a repeatable improvement on outcomes that matter for the agent’s job, with trace review showing that the change behaves as intended and no unacceptable regression in relevant costs or errors. Evidence is stronger when the same direction of improvement appears in production outcomes. An isolated higher score, a polished demonstration, or success on a broad public benchmark is weaker evidence if it does not reflect the agent’s task distribution or account for run-to-run variation.
For example, OpenAI reported that the best-performing tested agent setup in its 2025 PaperBench announcement achieved a 21.0% average replication score. That figure describes one setup on the PaperBench benchmark; it is not a threshold for judging whether an unrelated agent improved.
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