Definition #
Agent Testing and QA is the process of verifying that an Agent behaves correctly across key candidate paths before production release.
Why it matters #
Strong QA prevents broken flows, incorrect routing, and poor candidate experience in live environments.
How it works #
- Define critical path scenarios.
- Execute step-by-step tests using realistic candidate data.
- Verify data persistence and merge rendering.
- Validate integrations and completion outcomes.
- Record pass/fail and retest after fixes.
Configuration fields #
- Test candidate profile set
- Path/gate scenario matrix
- Required widget behavior checks
- Integration test endpoints/templates
- Expected outcomes per scenario
- QA sign-off status
Example scenario #
Before releasing a new Agent, QA runs scenarios for:
- successful completion path
- fallback path through gate conditions
- timeout behavior
- signing step completion
- final outcome assignment
Common mistakes #
- Testing only happy path
- Reusing stale candidate data between runs
- Not validating backward navigation behavior
- Ignoring timing-sensitive integrations
Troubleshooting checklist #
- Reproduce with fresh candidate token
- Validate each gate decision with actual input values
- Confirm step data saved before next render
- Check completion and outcome consistency
- Review logs for hidden partial failures
- Retest impacted scenarios after each fix