Give AI a job.
Check the work.
An evaluation is a test of whether an AI system does what you need. It can cover one answer, a repeating agent loop or a complete workflow.
- Agree on what good looks like.
Choose a real task. Write down the required result, what must never happen, and the limits on time, spend and access.
- Try the situations that matter.
Run normal tasks, awkward cases and failures. For a workflow, record the tool calls, retries and handoffs as well as the final answer.
- Decide what to change.
See which requirements passed, which failed and which were not tested. Fix the problem, repeat the test, and compare like with like before expanding use.
What that looks like in practice
Example task: an agent prepares an invoice for review. The test checks that it copies the right amounts, spots missing information and stops before payment approval. A convincing final message is not enough if it skipped a required check. This is an illustrative example, not a published result.
Check the cost of a completed job
Record model spend, elapsed time and retries where the runtime exposes them. Add human review and rework when comparing the workflow with your current process. Missing measurements stay marked as missing. A test result is evidence for a business decision, not an ROI guarantee.
Use the model that fits the task
The method is not tied to one provider. Compare models, instructions or workflows using the same tasks and rules. Each result should name the setup used and what the test can and cannot establish.