How to evaluate AI agents on real business tasks

Evaluate a business AI agent by checking the task outcome, the actions it took and the boundaries it respected. A fluent answer is only one part of quality. The evaluation should show whether the system produces useful work under ordinary conditions, missing information and tool failures.

Define success before collecting examples

Imagine an assistant preparing customer meeting briefs. A good brief must identify the right account, use current notes, distinguish facts from open questions and link to its sources. A polished summary of the wrong customer is a failure regardless of writing quality.

Ask the process owner to define unacceptable outcomes as well as ideal ones. This helps prevent an average score from hiding a serious permission or identity error.

Build a balanced set

• Typical inputs that represent routine work.
• Ambiguous requests requiring clarification.
• Incomplete or conflicting source information.
• Tool errors and interrupted actions.
• Requests outside the agent's permitted scope.

Use authorized examples or synthetic equivalents. Keep a separate set that was not used to tune the instructions, so the evaluation tests generalization rather than memorization.

Grade with observable criteria

Check destination records and source references directly where possible. Use human review for qualities such as usefulness and tone, with a shared rubric. If another model helps grade, validate its judgments against human examples instead of treating its score as ground truth.

Run important cases more than once when behavior can vary. Record model, instruction and tool versions so a later change can be compared fairly.

Turn failures into decisions

Classify each failure by cause: missing source, wrong retrieval, poor reasoning, permission failure or reporting error. Fix the relevant layer and rerun the affected cases alongside a normal case. The goal is not a perfect-looking dashboard; it is a clear basis for deciding which work the agent can safely own today.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.