Chapter 20: Agent Evaluation and Optimization
How do you know if your Agent is "good" or "not good"? Evaluation and optimization are the key to going from "usable" to "excellent."
Chapter Overview
Agent development doesn't end when you finish writing the code — you need to measure its performance, identify weaknesses, and continuously improve. This chapter introduces systematic evaluation methods, benchmarks, prompt tuning techniques, cost control strategies, and observability systems.
Chapter Goals
- ✅ Master the core methods of Agent evaluation (rule-based, LLM-based, human evaluation)
- ✅ Understand commonly used industry benchmarks and evaluation metrics
- ✅ Learn a systematic prompt tuning process
- ✅ Implement cost control and performance optimization
- ✅ Build a comprehensive observability system for Agents
Chapter Structure
| Section | Content |
|---|---|
| 20.1 How to Evaluate Agent Performance? | Evaluation dimensions, three evaluation methods |
| 20.2 Benchmarks and Evaluation Metrics | HumanEval, MMLU, custom benchmarks |
| 20.3 Prompt Tuning Strategies | System prompt optimization, A/B testing |
| 20.4 Cost Control and Performance Optimization | Model routing, caching, compression |
| 20.5 Observability | Logging, tracing, monitoring |
⏱️ Estimated Study Time
Approximately 90–120 minutes
💡 Prerequisites
- Completed Chapters 11–17 on framework practice and multi-Agent learning
- Have at least one runnable Agent project (to practice evaluation methods)
🔗 Learning Path
Prerequisites: Chapters 11–17: Framework Practice & Multi-Agent
Recommended next steps:
- 👉 Chapter 19: Security and Reliability — Security is also part of "quality"
- 👉 Chapter 20: Deployment and Production — Production optimization driven by evaluation metrics
Next section: 20.1 How to Evaluate Agent Performance?