Skip to content

evaluating-agent-performance.md

Latest commit

 

History

History
32 lines (21 loc) · 1.47 KB

evaluating-agent-performance.md

File metadata and controls

32 lines (21 loc) · 1.47 KB
Raw

Guide — Evaluating AI Agent Performance & Reliability

A practical guide to building benchmark datasets, step efficiency metrics, and regression testing pipelines for AI agent systems.

1. Why Agent Benchmarking Is Critical

Without standardized evaluation, prompt tweaks or model upgrades can silently degrade agent performance, increase token costs, or cause subtle regressions in tool precision.

2. The 5 Core Evaluation Metrics

  1. Task Pass Rate (%): Percentage of test benchmark tasks where the agent reached a verified solution.
  2. Step Efficiency Index: Average tool steps taken vs optimal ground truth path.
  3. Tool Call Error Rate (%): Percentage of tool invocations returning schema or execution errors.
  4. Token & Energy Budget Density: Total tokens consumed per verified outcome.
  5. Safety Violation Rate (%): Number of SafePath or permission violation attempts.

3. Building an Automated Evaluation Pipeline

Benchmark Tasks -> Agent Harness -> Isolated Workspaces -> Automated Test Asserts -> Scorecard Report
  1. Create Benchmark Dataset: Build 50-100 realistic task scenarios with expected assertion checks.
  2. Isolate Workspaces: Spawn clean, temporary workspace directories before each task run.
  3. Execute Assertions: Run deterministic test scripts (go test, lint checks, file diff asserts) after agent signals completion.
  4. Track Scorecard Trends: Compare pass rates across releases to catch regressions.