Build with evidence
Build an AI agent.
Know what to improve.
An AI agent builder helps you connect model calls, instructions, and tools into a working application. Stimulir supports the next part of that workflow: capture real traces, evaluate a controlled prompt or model change, and review the evidence before promotion. Use it alongside the code and agent framework you already maintain.
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
A support agent that keeps missing the next action
Work through a support-summary agent using your own application traffic. This is a reproducible example workflow, not a customer performance claim.
- 1
Connect the application
Route one model call through Stimulir, or import existing traces. Record the prompt key, version, model, and support-summary tag.
- 2
Find the repeated omission
Inspect summaries where the next action is missing. Curate eligible examples into a fixed cohort and define what a complete answer must contain.
- 3
Compare one change
Evaluate a revised instruction against the incumbent on the same cohort. Compare quality with latency and cost before preparing a promotion proposal.
- 4
Review before release
Keep the production label unchanged until a human reviews the results and explicitly approves promotion.
Choose the right layer
What each approach tells you
| Your starting point | What it gives you | Where Stimulir fits |
|---|---|---|
| A prototype or coding agent | Application code, instructions, and tool connections | Capture the behavior of the application you build. |
| An agent framework | Execution and orchestration for your workflow | Evaluate prompt or model changes using comparable cases. |
| A working production agent | Real inputs, outputs, and recurring failures | Turn selected failures into evidence for an improvement decision. |
Verify the details
Documentation you can inspect.
The quickstart documents tagged inference, trace inspection, prompt lineage, and the separation between evaluation and promotion. It includes a runnable Python integration example. Start there to verify the workflow with your own project.
Bring one live AI failure.
30 minutes with the founder. Bring one live AI failure; leave with an improvement plan.