# AI Agent Evaluation & Testing Tools: evaluation worksheet

Source: https://dutygraph.com/directory/ai-governance/categories/evaluation/
Editorial date: 2026-09-07

## Scope

- Organization / team:
- Task and expected output:
- Human owner:
- Product and version:
- Evaluation date / environment:
- Reviewer:

## Questions

### 1. Can the evaluation score actual tool behavior and final artifacts, not just response text?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

### 2. Who defines ground truth, and can disagreements between human reviewers be retained?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

### 3. Are model, prompt, dataset and scorer versions recorded for reproducibility?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

### 4. Can a regression block release, and what happens when results are uncertain?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

## Evidence checklist

- [ ] A versioned dataset with held-out cases
- [ ] Per-case results and error categories
- [ ] A release comparison and human review record

## Boundary to check

Passing a benchmark supports a conclusion about the tested cases. It does not prove performance for every customer, input or environment. Evaluation and production monitoring complement one another.

## Decision

- Fit for the scoped task:
- Unresolved gaps:
- Next action, owner and date:

This is a planning worksheet, not an endorsement, access approval or compliance certification. Keep confidential evaluation notes in your organization's approved storage.