Agent testingStaging evaluation
Find out how your agent behaves before your users do.
We run your AI agent through agreed scenarios in staging and compare what it does with what it should do. You get results you can trace, a rerun, and a clear note of where behaviour varied between runs.
1What we check
Behaviour, boundaries and consistency.
| Area | The question we test |
|---|---|
| Task outcome | Does the agent reach the agreed result for each scenario? |
| Tools and permissions | Does it use only the tools and actions it is allowed to, and stop where it should? |
| Escalation | Does it hand over to a person, or refuse, when the scenario calls for it? |
| Grounding | Are its answers supported by the sources it was given? |
| Errors and retries | What happens when a tool fails, times out or returns something unexpected? |
| Run-to-run variation | Do repeated runs of the same scenario agree? Where they don’t, we record it. |
2Inputs and outputs
What you provide
- Written authorization for a staging environment
- A frozen agent version and configuration
- The actions the agent is allowed to take
- Synthetic or approved test data
- Trace export, if your stack has one
What you receive
- Observed versus expected results for every agreed scenario
- Trace references for each result, where available
- A rerun of the agreed scenarios
- Notes on variation and on traces that were unavailable
3Not included
What this evaluation is not.
It is not penetration testing, red teaming, or a security certification, and it doesn’t cover production systems. Results describe the agreed scenarios on the version tested. They don’t predict behaviour on cases we didn’t run.
4Start
Close to a handoff, or about to change models?
Tell us what the agent does and what changed. We’ll propose scenarios and a scope.