Safety Evaluation / NAVIN RESEARCH
Evaluate AI Agents End to End
Measure task success, policy compliance, tool behavior, efficiency, and recovery across complete trajectories.
Measure task success, policy compliance, tool behavior, efficiency, and recovery across complete trajectories.
Short answer: Agent evaluation must inspect more than the final answer. Score whether the task was completed, tools were chosen and called correctly, evidence supports claims, policies were respected, and the system stopped efficiently or recovered safely.
Implementation steps
- Define outcome and process criteria from real tasks.
- Capture structured trajectories with redacted tool events.
- Use deterministic graders for actions and state changes.
- Add rubric or human review only for subjective dimensions.
Validation checklist
- Test repeated runs for variance.
- Include tool failures, stale data, and permission denial.
- Report results by task and risk slice.
Common mistakes
- Grading only the final prose.
- Rewarding longer trajectories.
- Letting the same model author every test and grade every result.
Practical decision
Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.
