/agent-evaluation

name: agent-evaluation
description: "Evaluate LLM agents and tool-using workflows—task success, tool accuracy, latency/cost, safety, and regression suites. Use when shipping agent features, comparing prompts/models, or debugging agent failures."
allowed-tools: Read, Glob, Grep

Agent evaluation

What to measure

| Dimension | Examples | |-----------|----------| | Task success | End state matches spec (binary or rubric) | | Tool use | Correct tool, valid args, no spurious calls | | Safety | No policy violations, no secret leakage | | Efficiency | Tokens, latency, tool call count | | Stability | Same input -> consistent outcome across runs |

Workflow

1. **Define tasks** — realistic user intents with clear pass/fail or scored rubric. 2. **Build dataset** — golden set + edge cases (errors, ambiguous input, empty context). 3. **Run baseline** — fixed model/settings; log traces (inputs, tools, outputs). 4. **Score** — automated checks first; human review for ambiguous cases. 5. **Compare** — A/B prompts, models, or tool schemas; report deltas with confidence notes. 6. **Gate** — block release on regression in must-pass tasks.

Automated checks

• Schema validation on tool arguments.
• Assert final answer contains required fields or avoids forbidden content.
• Snapshot tests for deterministic sub-steps where possible.

Human rubric (when needed)

Score 1-5 on: correctness, completeness, tone, safety. Document disagreements.

Anti-patterns

• Eval only on cherry-picked happy paths.
• Changing task and model simultaneously without isolation.
• No trace logs when debugging tool failures.

Output

Summary table: variant | success rate | avg tools | avg latency | notes.