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Evaluate an AI agent by checking whether it reliably achieves a verifiable task outcome—not just whether its final response sounds right. Run representative tasks repeatedly, inspect the full tool-use trajectory, verify important state changes, and compare success, process quality, cost, and end-to-end latency under documented conditions.
Define what success means before you choose metrics
Start with the job the agent is meant to do, then write down an outcome that can be checked. “Be helpful” is not a pass condition. For a booking agent, success might mean that a reservation exists and satisfies the user’s stated time, price, and airline constraints. A message claiming “Your flight is booked” is not evidence that the reservation was made.
For each test case, record the input, starting environment or state, permitted tools, success criteria, grader or graders, and resulting state. When the task changes something, verify the side effect in the system where it should occur, when feasible. Score separate properties separately—for example, whether a reservation exists, whether it matches the constraints, and whether the agent communicated the result accurately. Anthropic’s guidance distinguishes an agent’s claim from the environment’s actual state; Google Cloud likewise recommends defining measurable outcomes for the intended task (Anthropic; Google Cloud).
Build a representative test set and run repeated trials
Use tasks that resemble the work the agent will encounter, including difficult cases and known production failures. A broad benchmark score or a few convincing demonstrations cannot establish how well an agent handles your workload. OpenAI recommends task-specific evaluations, production-relevant data, logging, human calibration of automated graders, and continuous evaluation as the test set grows (OpenAI evaluation best practices).
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Agent behavior varies between runs, so run each relevant task multiple times. Report the number and type of tasks, the number of trials, and the system configuration and conditions. Preserve individual results: an aggregate can hide a task that succeeds only intermittently or a grader that marks a bad result as correct. Review traces from both failures and apparent successes. Anthropic recommends multiple trials for more consistent results, while OpenAI advises moving from individual traces to repeatable datasets and evaluation runs (Anthropic; OpenAI).
Keep the test conditions reproducible
Record the model and version, prompts, tools, routing, memory settings, retry policy, validators, and relevant environment details. These components form the agent’s harness: a change to any one of them can change its results. Keep a stable regression set for changes, and add cases that capture newly observed failures.
Score the result and the path the agent took
Outcome and trajectory answer different questions. A correct final answer can conceal an unsafe or unreliable process; a well-formed tool call does not prove that the task was completed.
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| Evaluation view | What to check | Why it matters |
|---|---|---|
| Outcome quality | Did the agent complete the intended task, return a correct and grounded result, and leave the environment in an acceptable state? | Confirms the user-visible result and, where relevant, the actual state change. |
| Process and trajectory | Did it choose appropriate tools, provide valid arguments, follow instructions and safety policies, avoid needless work, and recover sensibly from errors? | Finds silent failures, unsafe behavior, and fragile or wasteful paths that an outcome-only score misses. |
Inspect traces to see which tools were selected, what arguments were sent, where control was handed off, and how the agent responded to errors. Google Cloud describes a “silent failure” as a correct output produced through an inefficient or incorrect process. OpenAI’s trace-grading guidance also highlights tool choice, handoffs, policy violations, and whether a prompt or routing change improved end-to-end behavior (Google Cloud; OpenAI).
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Measure reliability, cost, and latency together
There is no single universal reliability threshold, cost figure, or latency target for AI agents. Set targets from the application’s needs, state test conditions, and compare quality and speed together: a faster run that fails is not a better result.
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| Measure | How to report it | What to include |
|---|---|---|
| Reliability | Successful trials divided by total trials, with task and trial counts and results by task. | Separate first-attempt success from success after retries; report safe recovery distinctly for consequential tasks. |
| Cost | Spend per attempt and expected spend per successful solve. | All model calls and relevant non-model charges, including retries, subagents, tools, sandbox compute, and third-party services. |
| Latency | End-to-end task time under a stated workload and measurement method. | Choose and disclose the statistics and service-relevant thresholds you use; the reviewed guidance sets no universal percentile, sample count, or latency target. |
Calculate cost across the whole task
Do not count only the final model call. OpenAI’s observability guidance identifies input tokens, cached input, output tokens, and reasoning tokens, and recommends accounting for retries, subagent work, tools, sandbox compute, and third-party charges. Cached input is still billed, and usage records can be incomplete or change as accounting arrives (OpenAI observability and usage).
For a simple comparison, divide total evaluation spend by the number of successful solves to estimate observed cost per success. This makes repeated attempts and retries visible in the comparison rather than treating an unsuccessful run as a useful low-cost solve. OpenAI’s third-party evaluation playbook likewise recommends considering expected cost per successful solve when comparing repeated attempts (OpenAI).
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Time the whole task, not just an isolated model response: tool calls, handoffs, retries, and waiting on external services may all contribute to the user’s elapsed time. State the workload and measurement conditions, then choose statistics and thresholds that match the application’s service needs. Faster completion is useful only when it meets the required quality and safety bar.
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Classify failures and check that the evaluator is trustworthy
A useful failure label points toward a fix. The categories below are a practical classification, not a standardized industry taxonomy.
| Failure category | Evidence to inspect |
|---|---|
| Task misunderstanding or ambiguous instructions | Input, instructions, and whether the intended success condition was clear. |
| Wrong or malformed tool call | Selected tool, arguments, tool response, and applicable tool rules. |
| Tool or service error | Service response, timeouts, and whether the agent handled the error appropriately. |
| Bad intermediate state or trajectory | Trace of actions leading to the final result, including unnecessary or unsafe steps. |
| Incorrect final response or unverified side effect | Final response compared with the environment’s actual state and the task criteria. |
| Unsafe or manipulated behavior | Trace, policy checks, and whether the agent followed untrusted or conflicting instructions. |
| Failed recovery | What happened after a tool error or other disruption, and whether a safe fallback was available. |
| Evaluator defect | Ground truth, grader instructions, task files, service reliability, and scoring rules. |
Audit the test itself as well as the agent. OpenAI’s third-party evaluation playbook identifies reward hacking, refusals, benchmark contamination, incorrect ground truth, ambiguous prompts, missing files, flaky services, unfair scoring, and exploitable shortcuts as threats to evaluation validity. Human review can materially change a result: the playbook describes an example in which review disqualified reward-hacked successes and revised an initial estimated time horizon from roughly 13 hours to roughly 6 hours. That is an illustration of how validity judgments can affect an estimate, not a general benchmark for other systems (OpenAI).
When an automated grader disagrees with a human or the environment, inspect the case rather than accepting the score on faith. Clarify the rubric, correct broken test data, and add a regression case if the agent or evaluator exposed a real failure.
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Compare agents without changing the question mid-test
First decide what you want to learn. To compare model capability, hold the harness as constant as practical. To compare application performance, test each system with its intended harness. In either case, document the task suite, prompts, tools, budgets, scoring rules, monitoring and review procedures, and versions. Different setups can change whether an agent completes the intended task or exploits a weakness in the evaluation.
A practical comparison scorecard can include verified task success, repeatability across trials, trajectory and tool quality, recovery and safety, latency under load, cost per attempt, cost per successful solve, and human review burden. Choose the measures and their importance based on the work the system must do; this is a decision framework, not a universal published score.
Treat example thresholds as task-specific
OpenAI’s evaluation best-practices examples include illustrative transcript-summarization thresholds of ROUGE-L 0.40 and at least 80% coherence, and document-Q&A thresholds of context recall at least 0.85, context precision above 0.7, and more than 70% positively rated answers. These are examples for those tasks, not general AI-agent benchmarks or recommended targets for unrelated applications (OpenAI).
Make evaluation a continuous loop
- Define the task: Write a verifiable success condition and identify any state change that must be checked outside the agent’s final response.
- Assemble realistic cases: Include representative work, edge cases, and failures observed in use; record the starting state and allowed tools.
- Run repeated trials: Keep the configuration and conditions with the results, and retain complete traces.
- Score both outcome and trajectory: Check task completion and state, then review tool use, policy adherence, and recovery.
- Calculate cost and latency: Include the full task’s calls and charges, and measure elapsed time under the documented workload.
- Review failures and graders: Classify what went wrong, check for evaluator defects or reward hacking, and correct invalid cases.
- Update the regression set: Turn confirmed failures into new tests and rerun the set after meaningful changes to the model or harness.
OpenAI’s product timelines can change: its evaluation best-practices documentation states that the Evals platform is scheduled to become read-only for existing users on October 31, 2026, and to shut down on November 30, 2026. Check the current documentation and the status of the specific tools before building a workflow around that platform (OpenAI evaluation best practices).
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