agent-evaluation

Based on Anthropic's "Demystifying evals for AI agents" Benchmarks : Grading Strategy : Key Metrics : Benchmarks : Grading Strategy (Multi-dimensional): Key Metrics : Grading Dimensions : Benchmarks : Grading Strategy : Solution : Add harder tasks, check for eval saturation (Step 7) Solution : Add more examples to rubric, use structured output, ensemble graders Solution : Use toon mode, parallelize, sample subset for PR checks Solution : Add production failure cases to eval suite, increase diversity - Designing evaluation systems for AI agents - Building benchmarks for coding, conversational, or research agents - Creating graders (code-based, model-based, human) - Implementing production monitoring for AI systems - Setting up CI/CD pipelines with automated evals - Debugging agent performance issues - Measuring agent improvement over time - Pros : Fast, objective, reproducible - Cons : Requires clear success criteria - Best for : Coding agents, structured outputs - Pros : Flexible, handles nuance - Cons : Requires calibration, can be inconsistent - Best for : Conversational agents, open-ended tasks - Pros : Highest accuracy, catches edge cases - Cons : Expensive, slow, not scalable - Best for : Final validation, ambiguous cases - SWE-bench Verified: Real GitHub issues (40% → 80%+ achievable) - Terminal-Bench: Complex terminal tasks - Custom test suites with your codebase - Test...