Chapter 6: System Evaluation, Debugging, and Performance Tuning
After designing and building multi-agent LLM systems, attention naturally shifts to their operational integrity and efficiency. This chapter addresses the post-development phases: evaluating system output, identifying and resolving problems, and enhancing overall performance.
You will learn to:
- Define and apply metrics to measure the effectiveness of your multi-agent systems.
- Implement robust logging and tracing for agent interactions to support analysis.
- Develop strategies for diagnosing complex behaviors and interaction failures.
- Identify bottlenecks and apply techniques to tune system performance and manage costs.
- Consider security practices and verification methods for your applications.
These skills are important for maintaining reliable and cost-effective multi-agent LLM deployments. We will cover systematic approaches to ensure your systems not only function as intended but also operate optimally.
Sections
6.1 Quantifying Multi-Agent System Effectiveness
6.2 Logging Mechanisms for Agent Activity Analysis
6.3 Diagnosing Complex Agent Behaviors
6.4 Identifying Performance Constraints and Optimization Points
6.5 Managing Operational Costs of Agents Using LLMs
6.6 Security Aspects of Multi-Agent System Design
6.7 Verification Methods for Multi-Agent Applications
6.8 Practice: Analyzing and Improving a Sample Multi-Agent System
© 2025 ApX Machine Learning