Test and quality-assure AI agents

Move from “it seems to work” to a repeatable quality decision. You define measurable quality, representative cases, blocking errors, human and automated graders, regression checks and a documented release decision.

The course has nine parts. You decide what quality means for the task, risk-classify failures, build a representative reference dataset covering normal, edge, missing, conflicting and untrusted cases, and write a rubric with six criteria.

You combine deterministic checks with human review, test difficult cases and stopping behaviour, compare versions to find regressions, and run a test lab with five cases plus a separate decision. The final test has eight questions and seven correct answers give you the certificate.

What you will be able to do

• ✓Build a test suite with representative cases
• ✓Measure quality in a comparable way
• ✓Set limits for when a human must review
• ✓Follow up on quality over time