Evaluation and Regression Gate

Create evaluation runs, submit results, compute metrics, save baselines, and triage regressions before shipping changes.

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# Evaluation and Regression Gate

Imported from curated first-party documentation sources.

## What this covers
Use this workflow to formalize benchmarking, regression detection, and baseline management around agent or product changes.

## Use this when
- Validating changes against prior baselines
- Making regressions visible before rollout
- Capturing repeatable evaluation evidence

## Expected outcomes
- Evaluation runs produce comparable metrics
- Baselines are stored after successful validation
- Regression triage becomes a workflow instead of an afterthought

## Source synthesis
- AGENT33/docs/functionality-and-workflows.md (https://github.com/mattmre/AGENT33/blob/main/docs/functionality-and-workflows.md)
- AGENT33/docs/use-cases.md (https://github.com/mattmre/AGENT33/blob/main/docs/use-cases.md)

## Dedupe notes
Combines AGENT33 evaluation lifecycle definitions with evaluation and regression use-case framing.

## Source excerpts

### AGENT33/docs/functionality-and-workflows.md
### 4.3 Evaluation Lifecycle

Flow:
1. Create run (`/v1/evaluations/runs`)
2. Submit task results (`/runs/{id}/results`)
3. Compute metrics + gate report
4. Save baseline (`/runs/{id}/baseline`)
5. Triage/resolve regressions

### AGENT33/docs/use-cases.md
## 4. Evaluation and Regression Gates

Goal:
- Quantify quality and block regressions across PR/merge/release gates.

Use these modules:
- `api/routes/evaluations.py`
- `evaluation/service.py`
- `evaluation/gates.py`
- `evaluation/regression.py`

Typical flow:
1. Create run for gate type (`G-PR`, `G-MRG`, `G-REL`, `G-MON`).
2. Submit task results and quality metadata.
3. Compute metrics and gate verdict.
4. Save baseline for future comparison.
5. Triage/resolve regression records.

Best fit:
- Teams with golden-task style quality gates.

Source

https://github.com/mattmre/AGENT33/blob/main/docs/functionality-and-workflows.md

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