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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsGood agent evaluation checks more than whether the final answer looks right. It should reveal whether the agent chose suitable tools, followed the intended workflow, respected guardrails, and completed the task reliably. These seven common evaluation mistakes—and their concise fixes—offer a practical way to make tests more useful and repeatable.
What should an AI agent evaluation measure?
An agent evaluation measures a system doing a task: its model behavior, tool selection and calls, handoffs, guardrails, and final result. A polished response can hide a failed workflow; a flawed task or grader can make a capable system look unsuccessful. OpenAI’s guidance distinguishes inspecting individual traces for debugging from running dataset-based evaluations to compare behavior across versions (Evaluate agent workflows).
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Seven mistakes that make agent evaluations misleading
1. Scoring only the final answer
A correct-looking answer does not prove that the agent took a sound path. It may have selected the wrong tool, missed a needed handoff, or violated an instruction along the way.
One-line fix: Grade representative end-to-end traces for the decisions and transitions that determine success.
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OpenAI describes a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run, and recommends trace grading to assess workflow behavior (Trace grading).
2. Starting without examples or a definition of “good”
A score is hard to interpret if the test cases do not resemble real tasks or if success has not been defined. Without shared criteria, a change can move a metric without improving the work users need done.
One-line fix: Gather representative task examples and define success criteria before comparing versions.
OpenAI’s evaluation guidance presents data collection, metric definition, and comparisons as parts of an evaluation workflow (Evaluation best practices).
3. Treating an LLM judge as ground truth
A model grader can handle flexible responses, but its decision is still a judgment—not an unquestionable label. Ambiguous tasks, flaws in the grader, or constraints in the test harness can produce misleading failures.
One-line fix: Use deterministic grading when the result is directly checkable, and investigate disagreements between the grader, task, and harness.
Anthropic’s guide discusses grader selection and how grader defects, harness constraints, and task ambiguity can affect agent evaluations (Demystifying evals for AI agents).
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4. Using open-ended generation scores when a bounded judgment fits better
Some questions are unnecessarily difficult to judge as free-form text. If the evaluation is asking which of two outputs is better, whether a category applies, or whether explicit criteria were met, shape the grading task around that judgment.
One-line fix: Use a comparison, classification, or rubric-based score when it matches the behavior being tested.
OpenAI’s guidance says LLMs are better at discriminating between options and recommends these bounded approaches where appropriate (Evaluation best practices).
5. Running an ad hoc suite that cannot be repeated
Inspecting one trace can help diagnose a bug, but a collection of one-off checks cannot reliably show whether a prompt, model, or workflow change improved performance. Repeated evaluation requires cases that can be run against successive versions.
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OpenAI’s agent-workflow guidance separates trace inspection for debugging from dataset-based evaluation for benchmarking and comparing changes (Evaluate agent workflows).
6. Ignoring variability across runs
One successful run does not show that an agent will behave consistently. Where responses or tool choices can vary, a single result may hide intermittent failures.
One-line fix: Repeat cases where variability matters, and keep evaluating as the application changes.
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OpenAI recommends ongoing evaluation and monitoring for nondeterminism; Microsoft’s agent framework guidance likewise recommends running each query multiple times to detect it (Evaluation | Microsoft Agent Framework).
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7. Assuming the evaluation platform’s status will stay the same
Evaluation tools and APIs have lifecycles. A workflow that depends on a particular platform should not be treated as permanently available just because its documentation once described it.
One-line fix: Check the official lifecycle notice before relying on a platform or describing its availability.
As of the OpenAI evaluation-best-practices page checked on October 7, 2026, it stated that the Evals platform would become read-only on October 31, 2026, and was scheduled to shut down on November 30, 2026 (Evaluation best practices). Those are dated lifecycle details, not timeless availability guarantees.
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How to combine these fixes into a workable evaluation loop
- Start with real tasks. Assemble representative examples and write down the observable criteria that define success.
- Inspect the whole run. Use traces to examine tool choices, calls, handoffs, guardrails, and outcomes—not just the last message.
- Match the grader to the criterion. Prefer deterministic checks for directly verifiable results. Use comparisons, classifications, or explicit rubrics for judgments that need interpretation, and review disagreements.
- Make comparisons repeatable. Run a dataset against versions you want to compare, rather than relying on isolated spot checks.
- Repeat variable cases and revisit the suite. Run cases more than once when nondeterminism matters, and update evaluation as the application evolves.
- Verify dependencies. Check current official lifecycle information before building a process around a platform.
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