# Chapter 18: 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."

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## 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 |
|---------|---------|
| 18.1 How to Evaluate Agent Performance? | Evaluation dimensions, three evaluation methods |
| 18.2 Benchmarks and Evaluation Metrics | HumanEval, MMLU, custom benchmarks |
| 18.3 Prompt Tuning Strategies | System prompt optimization, A/B testing |
| 18.4 Cost Control and Performance Optimization | Model routing, caching, compression |
| 18.5 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](../chapter_langchain/README.md)
>
> **Recommended next steps**:
> - 👉 [Chapter 19: Security and Reliability](../chapter_security/README.md) — Security is also part of "quality"
> - 👉 [Chapter 20: Deployment and Production](../chapter_deployment/README.md) — Production optimization driven by evaluation metrics

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*Next section: [18.1 How to Evaluate Agent Performance?](./01_evaluation_methods.md)*