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To tell whether an AI agent actually got better, compare the old and new versions on the same representative tasks, using the same success criteria and grading method. Repeat runs when outputs vary, inspect traces to see what changed, and track relevant costs and errors alongside task outcomes. A higher score is evidence about the cases tested—not proof of a broad or lasting improvement.
Define what “better” means for this agent
Start with the job the agent is meant to do, then describe what a successful run looks like in observable terms. Depending on the task, that might mean producing a correct result, taking required tool actions, completing safely, or escalating appropriately when it cannot proceed.
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Anthropic defines an evaluation as a test that gives an AI an input and applies grading logic to measure success in its engineering guide, Demystifying evals for AI agents. The important practical point is to choose tests and grading rules for your application; a general model benchmark is not a substitute for defining the outcome your agent must deliver. OpenAI’s agent evaluation guidance likewise describes evaluating agent workflows with task-specific data and graders.
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Preserve a representative task set
Create a curated set of real or realistic tasks that reflects how the agent is expected to be used. Record the expected outcome or grading rubric for each case. Keep a stable subset so you can compare versions over time, and add newly observed failures or changed requirements deliberately. OpenAI describes datasets and evaluation runs as a way to benchmark agent changes; Anthropic recommends static task banks for baselines and regression measures.
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Run both versions under the same conditions
Use the same cases, grading rules, and evaluation setup for the baseline and the changed agent. Identify the versions and record configuration changes so you can repeat the comparison and understand what changed. If outputs or tool decisions vary between runs, repeat the evaluation rather than treating one result as stable. OpenAI’s evaluation best practices discuss variability and the need to monitor nondeterministic behavior.
Score outcomes and inspect the traces
Use deterministic checks when a result is directly verifiable, such as whether a required field is present or a specified action occurred. Use a rubric or human review for qualities that require judgment. Do not rely on the final answer alone: examine traces for cases where the result changed or failed.
A trace can show tool calls, outputs, intermediate results, and where a workflow went off course. Anthropic’s guide explains traces as records of an agent trial, while OpenAI’s trace grading documentation describes grading traces to identify errors and compare behavior across examples. This can distinguish a genuine improvement in task completion from a change that merely produces a more convincing-looking answer.
Compare the dimensions that matter
A version may complete more tasks but take longer, use more tokens, or incur more errors. Choose measures that matter for the agent’s job and consider them together rather than treating one score as the whole verdict.
| Dimension | What to examine |
|---|---|
| Task outcome | Whether the user’s intended job was completed correctly. |
| Workflow behavior | Tool choice and execution, intermediate steps, and the point of failure visible in traces. |
| Consistency | How much results vary across repeated runs when behavior is variable. |
| Operational measures | Latency, token usage, cost per task, and errors, where relevant to the application. |
| Transfer to real use | Whether the improvement appears in production outcomes and current user tasks. |
Anthropic’s guide identifies latency, token usage, cost per task, and error rates as measures that can be tracked on a static task bank. The right balance depends on the task and the consequences of failure.
Interpret score changes cautiously
A small or narrow test set can make a change look better or worse because of which examples happened to be included. Performance on a finite benchmark sample may also differ from performance on a broader, unseen task universe; Anthropic’s statistical approach to model evaluations discusses this limitation. There is no universal score increase or sample size that proves an arbitrary agent improved.
For perspective, OpenAI reported a 21.0% average replication score for the best-performing tested agent setup in its 2025 PaperBench announcement. That number describes a result on that benchmark and setup; it is not a general threshold for evaluating other agents.
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Check production results and refresh the tests
Offline evaluations make version-to-version comparisons repeatable, but they cannot establish by themselves that gains transfer to real interactions. Compare recent production runs with actual outcomes where you can do so appropriately. LangSmith’s documentation describes both curated offline evaluation and comparison of production runs to outcomes in its evaluation types guide.
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Keep the task set current. A fixed benchmark can stop being informative when the agent passes the tasks it can solve, or when those tasks no longer reflect current requirements. Add relevant new cases and revisit expected outcomes when the job changes, while retaining a stable subset for historical comparisons.
What counts as convincing evidence?
The strongest practical case is a repeatable gain on outcomes that matter, supported by trace review showing the agent is behaving as intended, without an unacceptable regression in other important dimensions. Confidence rises when the same pattern also appears in relevant production outcomes. A single higher score, polished demonstration, or strong result on an unrelated public benchmark is weaker evidence because it may not reflect the agent’s real task mix or run-to-run variation.
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