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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsBuild effective AI agent evals by testing the complete system—the model, harness, tools, routing, and environment—against clear, observable task outcomes. Score both what the agent accomplished and how it behaved, repeat runs when variability matters, then inspect failed traces before changing the agent or the test.
What makes an AI agent eval different?
An agent works across turns, may call tools, and can change state in an environment. A final answer can sound successful even when the requested action never happened, so an eval should verify the outcome in the environment as well as assess the response and the path taken.
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Anthropic summarizes the scope this way: “When we evaluate ‘an agent,’ we’re evaluating the harness and the model working together.” That framing is useful whether you build your own harness or use an evaluation platform: a result belongs to the tested system and its conditions, not to the model in isolation. Anthropic, “Demystifying evals for AI agents”
1. Define the task and observable success criteria
Start with real tasks the agent is supposed to perform. For each case, specify the request, relevant context or starting state, tools available, constraints, and evidence that would prove success. Prefer a check on the environment—such as whether the intended record exists—over accepting the agent’s claim that it created one.
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Do not make one exact sequence of tool calls the answer key unless the task genuinely requires that sequence. When multiple paths can achieve the same valid result, grade the required outcome and only those behavioral constraints that matter. This avoids marking a sound alternative as a failure. Anthropic’s guidance on agent evals
Write each case so another person can reproduce it
Record the task input, context or state, available tools, constraints, success conditions, and grader. For a multi-turn workflow, include the conversation and state needed to reproduce it. A useful case makes it clear what is being tested, what evidence counts, and what should happen when the agent cannot complete the task.
2. Build a representative dataset
Seed the dataset with production examples where appropriate, user feedback, and cases from people who understand the domain. Include routine tasks as well as difficult edge cases tied to likely failure modes. For extended workflows, simulated user responses can exercise interactions that a single prompt cannot cover.
Keep some examples held out from the changes being tuned so you can check whether an apparent improvement generalizes. OpenAI’s evaluation guidance and Google Cloud’s agent-evaluation guidance both describe building evaluations around representative examples rather than relying on one-off demonstrations. OpenAI’s evaluation best practices; Google Cloud’s agent evaluation guidance
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3. Run the agent in the system it will actually use
Evaluate the model together with the harness, tools, routing, guardrails, and environment that shape its work. Save the trace: model calls, tool calls and arguments, handoffs, guardrail events, intermediate results, and final environment state. Also record the configuration and version used for each run so comparisons remain interpretable.
OpenAI documents workflows for repeatable agent evaluation and trace grading; Google Cloud documents agent-evaluation workflows in its platform. These are implementation examples, not independent evidence that one platform is best. OpenAI’s agent workflow evaluation guide; Google Cloud’s agent evaluation guide
4. Match each grader to what it needs to judge
Use more than one grader when a task has distinct requirements. A single combined score can conceal a serious failure, such as an unsafe action masked by a polished response.
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- Model-based rubrics: Use for nuanced judgments such as helpfulness, completeness, or tone. Write explicit criteria and examples so the grader has a defined standard.
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Anthropic’s agent-evaluation guidance recommends selecting graders to match the criterion and using multiple assertions where needed. Anthropic’s grader guidance
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5. Measure outcomes and relevant behavior
Choose measures tied to product requirements. A compact scorecard can include the following, with only the dimensions that matter for the agent’s job:
- Task outcome: Did the requested work complete in the environment?
- Response correctness: Was the final answer accurate and relevant?
- Instruction following: Did the agent obey task and system constraints?
- Tool use: Did it choose an appropriate tool and provide valid arguments?
- Safety: Did it avoid prohibited or unsafe actions?
- Handoffs: Did it transfer control to another agent or a human at the right point?
- Trajectory quality: Were important actions present and sensibly ordered, without demanding an unnecessarily rigid path?
Text-generation measures such as coherence do not, by themselves, establish that an agent completed work in an environment. OpenAI’s evaluation best practices and Google Cloud’s agent-evaluation material discuss evaluating behavior and task performance beyond final text. OpenAI’s evaluation best practices; Google Cloud’s overview of agent evaluation metrics
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Repeat trials and compare changes fairly
Model behavior can vary, so a single attempt is weak evidence when the decision depends on reliability. Run multiple trials when the task’s variability and the consequences of failure justify the cost. Compare prompt, tool, routing, or guardrail changes on the same dataset and under compatible run conditions.
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Report the number of trials, tested configuration, and aggregation method alongside results. The reviewed guidance does not establish a universal trial count or passing threshold; choose them for the task and its risk rather than treating a single score as a general standard. Anthropic’s agent-evaluation guidance; OpenAI’s agent workflow evaluation guide
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7. Diagnose failures before changing the agent
When a score changes, inspect representative traces and the grader’s assertions or reasoning. Separate an agent error from an evaluation defect: an unclear task, brittle exact-match expectation, restrictive harness, or irreproducible stochastic setup can produce a misleading result. Check whether the environment actually reached the intended state and whether the grader recognized valid alternative solutions.
Anthropic reported a benchmark-specific example: Opus 4.5 initially scored 42% on CORE-Bench; after issues including rigid grading, ambiguous task specifications, and stochastic tasks were identified and addressed, alongside a less constrained scaffold, its score rose to 95%. Those figures describe that benchmark example, not typical or general AI-agent performance. Anthropic’s account of CORE-Bench evaluation issues
8. Make evals part of development and production learning
During debugging, inspect traces directly. Turn established examples into repeatable datasets and eval runs; after a targeted change, rerun the relevant suite and check for regressions beyond the intended case. Production monitoring, user research, and A/B testing provide additional evidence that pre-release evals alone cannot supply. OpenAI’s agent workflow evaluation guide; Anthropic’s agent-evaluation guidance
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Keep the following together so a score can be traced back to the conditions that produced it:
Quick Recap
- The task, context, and starting environment state
- The available tools and constraints
- The trial and tested system configuration or version
- The grader, its criteria, and its result
- The trace, including tool activity and handoffs
- The resulting environment state
- The dataset or broader suite and the method used to aggregate results
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