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What should an AI agent evaluation measure?
Start with the job the agent is supposed to do, then turn “good” into a condition you can check. For a booking agent, for example, success is not simply being helpful or saying a flight was booked: it is creating a reservation that meets the user’s time, cost, and airline constraints. Google Cloud uses this kind of measurable task outcome to illustrate evaluation; Anthropic likewise distinguishes an agent’s claim from the environment’s actual final state. (Google Cloud, November 17, 2025; Anthropic, January 9, 2026)
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For each test case, record the input, starting state or environment, available tools, success criteria, grader or graders, and final state. A single task may need separate checks—for example, whether the result is factually correct, whether a required action occurred, and whether the agent respected constraints. When the task changes external state, verify that change in the relevant system where possible. A convincing response is not evidence that an email was sent, a record was updated, or a booking exists.
How do you build a realistic evaluation set?
Use tasks that resemble the work your agent will actually receive, including difficult cases and known failure patterns. A broad benchmark can be informative, but it cannot by itself establish performance on your application’s traffic, tools, or constraints. OpenAI recommends task-specific evaluations, production-relevant data, logging, human calibration of automated graders, and expanding the evaluation set as new cases arise. It cautions that generic metrics or datasets that do not reflect real traffic can give a misleading picture. (OpenAI, “Evaluation best practices”)
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Keep the conditions attached to the results. Report which tasks were run, how many trials each received, and the configuration used—including model, prompt, tools, routing, memory, retries, validators, and environment. The evaluation target is the deployed system or harness, not an abstract model in isolation: these components can change what the agent can do and how it behaves. Anthropic describes the harness as the system that enables the model and orchestrates tools; OpenAI’s third-party evaluation playbook also warns that setup details can affect whether a system completes the intended task or exploits the evaluation. (Anthropic; OpenAI)
One attempt is not a reliable characterization of a variable system. Run multiple trials and retain complete traces for both failures and apparent successes. Anthropic recommends multiple trials because outputs vary between runs; trace review can reveal inconsistent behavior, tool-selection errors, silent failures, or a grader that awarded credit incorrectly. An aggregate score without task-level results can conceal all of these. (Anthropic; OpenAI, “Evaluate agent workflows”)
How should you score the outcome and the agent’s process?
Use two complementary views. Outcome scoring asks whether the intended task was completed correctly and left the environment in an acceptable state. Trajectory scoring asks whether the agent took an appropriate, safe, and efficient path: which tools it chose, whether arguments were valid, how it handled handoffs and errors, and whether it followed instructions and safety policies.
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This distinction catches “silent failure”: an agent can produce a correct answer through an inefficient or incorrect process, such as relying on the wrong source. Conversely, a useful process does not excuse an incorrect end result. Google Cloud’s framework covers agent success and quality, process and trajectory, and trust and safety under non-ideal conditions. OpenAI’s trace-grading guidance suggests assessing tool choice, handoffs, policy violations, and whether changes to prompts or routing improve end-to-end behavior. (Google Cloud; OpenAI)
Where available, a structured trace evaluator can help make these checks repeatable. Google Cloud’s Gen AI agent evaluation documentation describes final-response and trajectory evaluation, with per-instance fields including latency_in_seconds and failure. That specific feature is marked Preview and subject to Pre-GA terms, so its status and terms should be checked before adopting it. (Google Cloud documentation)
How do you measure reliability, cost, and latency?
| Measure | How to evaluate it | What to report |
|---|---|---|
| Reliability | Count verified task successes across repeated trials. For tasks with retries, distinguish first-attempt success from eventual success and record whether recovery was safe. | Success rate, task and trial counts, variability, and the retry conditions. The reviewed guidance establishes no universal reliability threshold. |
| Cost | Sum the resources used for the whole task, not just the final model call. Include failed attempts and any retries or delegated work. | Cost per attempt and expected cost per verified successful solve, with the accounting scope and any incomplete usage records noted. |
| Latency | Measure end-to-end task time under a stated workload, including relevant tool or service waits. | Measurement conditions and a statistic or service target chosen for the application. The reviewed guidance does not prescribe a universal sample count, percentile, or latency threshold. |
Reliability: count the outcome, not the claim
Define the denominator clearly: specify which tasks and trials count, and whether success means a correct answer, a verified environmental change, or both. Keep first-attempt success separate from eventual success after retries so retries do not hide an unreliable first run. For consequential workflows, record whether an error was contained and recovered from safely. These distinctions are practical reporting choices, not a universal reliability standard.
Cost: include the full task
Count all model calls and relevant non-model charges needed to finish the task. OpenAI’s observability guidance identifies input tokens, cached input, output tokens, and reasoning tokens as usage fields, and advises accounting for retries, subagent work, tools, sandbox compute, and third-party services. Cached input is still billed; usage records may also be incomplete or change as accounting arrives. (OpenAI, “Observability and usage”)
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A useful comparison is total measured cost across attempts ÷ number of verified successful solves. This expected cost-per-success view captures systems that are cheap per attempt but often fail, or that need repeated calls before they complete the work. OpenAI’s third-party evaluation playbook recommends considering expected cost per successful solve rather than only success at a fixed token budget. State what the calculation includes so that two systems’ figures are comparable. (OpenAI)
Latency: measure the user-visible job
Measure from the start of the task to its usable completion under a declared workload. Include relevant orchestration and tool time rather than comparing only model response time. Pick a statistic and target that reflect the application’s service needs, and report the conditions; do not treat a faster run as better if it fails the task or falls below the required quality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you classify failures and check the evaluator?
Preserve the trace and enough environment evidence to identify where the run went wrong. A practical taxonomy includes:
- Task understanding: the agent misunderstood the request or the instructions were ambiguous.
- Tool use: it chose the wrong tool, supplied malformed arguments, or encountered a tool or service error.
- Trajectory or intermediate state: an earlier action put the task on the wrong path or left the environment in a bad state.
- Outcome verification: the final response was incorrect, or it claimed a side effect that did not occur.
- Safety or manipulation: the agent behaved unsafely or was manipulated into violating constraints.
- Recovery: it did not recover sensibly from an error or failed to contain its effects.
- Evaluator defect: the ground truth, grader, task setup, or service was broken or unfair.
This is a useful working classification, not a standardized industry taxonomy. OpenAI’s third-party evaluation playbook specifically flags reward hacking, refusals, benchmark contamination, and flawed tasks—for example, incorrect ground truth, ambiguous prompts, missing files, flaky services, unfair scoring, or shortcuts exposed by the environment. (OpenAI)
Inspect apparent successes as well as failures: an agent can earn a good score by exploiting a shortcut that does not solve the real job. The playbook gives an example in which human review of reward-hacked successes changed an initial estimate of roughly 13 hours to roughly 6 hours. That example shows how validity judgments can materially change an evaluation estimate; it is not a general performance figure for agents. Review graders against human judgments and check whether tasks and infrastructure behaved as intended before drawing conclusions from a score.
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How do you compare two agents fairly?
First decide what question the comparison answers. 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, monitors, review procedures, and versions; changing these can change both task success and susceptibility to shortcuts. (Anthropic; OpenAI)
Build a scorecard that keeps distinct trade-offs visible rather than collapsing everything into one score:
- Verified task success and consistency across trials
- Tool and trajectory quality
- Safe recovery and policy compliance
- End-to-end latency under the stated workload
- Cost per attempt and per verified successful solve
- Human review burden
Choose weights only if the application genuinely requires a single ranking, and state them. A latency improvement should not conceal worse task success, and a higher success rate should not conceal unacceptable cost or unsafe recovery.
Are published evaluation thresholds universal targets?
No. OpenAI’s evaluation best-practices page gives illustrative task-specific examples, not general agent benchmarks: transcript summarization examples include a ROUGE-L threshold of 0.40 and at least 80% coherence; a document question-answering example uses context recall of at least 0.85, context precision over 0.7, and more than 70% positively rated answers. Those values depend on their example tasks and should not be adopted as universal pass marks. Set thresholds against the requirements and error costs of the actual job. (OpenAI)
The same caution applies to headline reliability, cost, and latency numbers: the reviewed sources do not establish an independent cross-vendor statistic for general AI-agent performance. A credible result needs its task distribution, setup, repetitions, graders, and measurement conditions—not just a percentage or a benchmark name.
What evaluation tooling caveats matter in 2026?
OpenAI’s evaluation best-practices page states that its Evals platform is scheduled to become read-only for existing users on October 31, 2026, and to shut down on November 30, 2026. Its agent-workflow guide separately describes traces, graders, datasets, and evaluation runs. Because these are product timelines and surfaces that can change, check the current status of the specific tool before planning an implementation around it. (OpenAI, “Evaluation best practices”; OpenAI, “Evaluate agent workflows”)
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