Having constructed agentic systems incorporating reasoning, memory, and tool capabilities, the subsequent requirement is to systematically assess their performance and refine their operation. This chapter introduces the methodologies essential for this phase. You will learn to establish meaningful success metrics appropriate for agentic tasks, which often go beyond simple accuracy measures like .
We will examine techniques for evaluating the core components: assessing the quality of the reasoning and planning processes, verifying the reliability and effectiveness of tool utilization, and measuring the performance of integrated memory systems using metrics such as retrieval precision and recall. Additionally, the chapter covers the use of established benchmarks for comparative analysis, practical debugging strategies tailored to complex agent behaviors, and introduces optimization techniques aimed at improving system speed, cost-efficiency, and overall dependability, including the fine-tuning of LLMs for specialized agent roles.
6.1 Defining Success Metrics for Agentic Tasks
6.2 Evaluating Reasoning and Planning Capabilities
6.3 Assessing Tool Use Reliability and Accuracy
6.4 Memory System Performance Evaluation
6.5 Benchmarking Agentic Systems (AgentBench, etc.)
6.6 Debugging Strategies for Complex Agent Behavior
6.7 Optimization Techniques for Agent Performance
6.8 Fine-tuning LLMs for Specific Agent Roles
6.9 Practice: Setting up an Evaluation Framework
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