Comprehensive evaluation framework for designing, building, and monitoring AI agent performance across coding, conversational, research, and computer-use agents.

• Covers three grader types (code-based, model-based, human) with trade-offs and best practices for each agent category
• Provides an 8-step roadmap from initial task creation through production monitoring, including environment isolation, outcome-focused grading, and saturation detection
• Includes benchmarks for major agent types: SWE-bench for coding, WebArena for computer use, τ2-Bench for conversational agents
• Offers CI/CD integration patterns, A/B testing templates, and production sampling strategies for real-time quality monitoring