Reliability and evaluation
A workflow that worked at launch can quietly stop working. Evaluation prevents problems before launch. Observability catches them after. Together, they make the intelligence accountable - not magical.
Evaluation - testing before it goes live
Evaluation checks quality and safety. The workflow is tested on your real cases, against signed criteria, before anything goes live. Not a demo - your work, your standards.
Test before launch
The workflow is tested on 50+ of your real cases - pass or fail, against signed criteria. Not a demo. Your work, your standards.
Real-case evaluation
Each test case checks quality and safety. Does the workflow do what was agreed? Does it handle edge cases correctly? Does it refuse when it should?
Escalation cases
We test what happens when things go wrong - a system is down, data does not match, or the case falls outside scope. The workflow should escalate, not force through.
Refusal cases
We test that the workflow refuses to act when it should - when confidence is low, when an approval is missing, when the action is outside agreed boundaries.
Observability - explaining behavior in production
Observability explains what the workflow is doing in production - through logs, traces, and metrics. Evaluation prevents problems. Observability catches them live. The two are separate concerns.
Failure logging
When a step fails, the failure is logged with full context - what was attempted, what went wrong, and what happened next. No silent failures.
Behavior traces
Every action is traceable end-to-end. You can see the full path from input to outcome - what context was gathered, what rules applied, what path was taken.
Behavior monitoring
Live metrics track how the workflow is performing - pass rates, exception rates, processing times, and drift from the baseline established at launch.
Drift alerts
When behavior drifts from the baseline - after a model update, a system change, or a process change - you are alerted. The workflow is re-tested before it continues.
Re-validation after changes
The tools underneath change. Your systems change. Your business changes. When they do, the workflow is re-tested against your own cases - so an update means a re-test, not a rebuild.
- When a model release changes behavior, the workflow is re-tested on your cases
- When a system you depend on changes its API, the integration is re-validated
- When your process changes, the rules and approvals are updated and re-tested
- When an edge case is found in production, it becomes a new test case