Platform / Evaluation
Measuring performance instead of assuming it.
Evaluation is what turns 'it worked in the demo' into a claim you can defend — a baseline, a regression suite, and an ongoing check against drift.
What gets measured
A representative scorecard for a production agent.
Illustrative categories — the specific metrics and thresholds are defined per workflow.
Task accuracy
Scored per run
Regression suite
Run on every change
Drift check
Reviewed against baseline
Escalation rate
Tracked over time
Key concepts
How evaluation stays continuous.
Scoring
Each run is scored against defined success criteria, not just "did it complete."
Regression suites
A representative set of cases is re-run whenever the agent, prompt, or tools change.
Drift detection
Performance on live traffic is compared against the baseline to catch slow degradation.
Measurable improvement
Evaluation results feed back into what gets adjusted, so change is driven by evidence.
Limitations
What evaluation can't tell you on its own.
A regression suite is only as good as the cases in it. Coverage is built from real examples of the workflow, and it grows as edge cases are found — it isn't exhaustive on day one.
{{TBD: source needed}} — specific benchmark numbers for a given workflow depend on the data and success criteria defined during the pilot, not a generic industry figure.
Define success criteria before you build.
A working session starts with what 'working' means for your workflow, so evaluation has something to measure against.