Evaluate one controlled change
A better answer needs
a fair comparison.
AI agent evaluation measures how an agent performs on defined tasks before you change its production behavior. Stimulir uses captured or imported traces to form an evaluation cohort, compare an incumbent with a prompt or model candidate, and review quality, latency, and cost. Promotion remains a separate human decision.
Updated
Explore the workflow in the docs- 01Capture a trace
- 02Fix the cohort
- 03Compare a change
- 04Review the evidence
Production promotion requires separate human approval.
One practical workflow
Check whether a cheaper model preserves support quality
Use a support workflow with recorded prompt lineage and an enabled alternative model. Treat lower cost as one criterion alongside task quality, not as proof of improvement.
- 1
Define the task
Choose representative support cases and explicit criteria: correct answer, required next action, and no unsupported claims. Check privacy and missing lineage before running.
- 2
Fix the comparison
Preview the exact eligible cohort. Keep the task, prompt, and evaluation criteria fixed while comparing the incumbent with one model candidate.
- 3
Read the evidence
Review individual failures as well as quality, pass rate, latency, cost, and accounting confidence. A small setup check does not establish production readiness.
- 4
Make the decision
Retain, reject, or iterate on the candidate. Prepare a promotion proposal only when sufficient comparable evidence supports the change.
Choose the right layer
What each approach tells you
| Approach | Useful for | What it leaves unresolved |
|---|---|---|
| Try a few prompts manually | Checking the integration and spotting obvious failures | Coverage, repeatability, and regression risk. |
| Inspect production traces | Seeing what happened on real requests | Whether a proposed change does better on comparable inputs. |
| Run a controlled evaluation | Comparing a defined candidate against an incumbent | Whether the cohort represents every production condition. Human review still matters. |
Verify the details
Documentation you can inspect.
The Improve reference specifies inspection, privacy-checked previews, controlled runs, result review, and explicit promotion boundaries. The quickstart also documents the evaluation skill and the measurements to request at completion. These are verifiable workflow capabilities; no universal quality uplift is promised.
Bring one live AI failure.
30 minutes with the founder. Bring one live AI failure; leave with an improvement plan.