Introduction to Agent Evaluation
Master systematic evaluation of AI agents to ensure they meet production requirements
Your Progress
0 / 5 completedThe 5-Stage Evaluation Framework
Effective evaluation follows a structured process. This framework ensures you systematically assess agent performance, identify gaps, and drive continuous improvement. Think of it as a scientific method for validating AI agents—hypothesis (success criteria), experiment (testing), observation (measurement), analysis (results), and iteration (improvement).
Interactive: Build Your Evaluation Plan
Click through each stage to understand the framework and build your evaluation approach:
1. Define Success Criteria
What does "good" look like for your agent?
Clear success metrics with target thresholds
📊 Quantitative Metrics
Numbers you can measure: accuracy, latency, cost, uptime
💬 Qualitative Feedback
User satisfaction, output quality, edge case behavior
Don't try to evaluate everything at once. Start with 2-3 critical metrics (e.g., task success rate, response time, error rate). Once you have a baseline and improvement process, add more metrics. Comprehensive evaluation frameworks are built incrementally, not all at once.