Multi-Agent Systems › Summary

Multi-agent systems extend single-agent capabilities through parallelism and specialization coupled with independent verification. The key concepts covered in this chapter are:

• Topologies: Hierarchical orchestration (most common), peer-to-peer, and pipeline patterns each suit different task structures. Real systems often combine multiple topologies.
• Communication: Shared workspaces and message passing are complementary paradigms. Messages need explicit types, conversation IDs, and structured payloads to support reliable coordination.
• Role assignment: Static roles are predictable; dynamic roles are flexible. Specialization happens primarily through system prompts and tool access.
• Coordination mechanisms: Dependency DAGs ensure correct execution order, voting and judge agents resolve disagreements, and retry logic with fallback agents provides fault tolerance.
• Benchmarks: Task-level benchmarks like SWE-bench and WebArena measure real-world coordination quality. Metrics beyond accuracy (coordination overhead, parallelism utilization, recovery rate) capture multi-agent-specific behavior.
• Limitations: Error accumulation, communication overhead, security risks from inter-agent message injection, and debugging complexity are inherent costs. Use multi-agent approaches when these costs are justified by the task complexity.

The coming chapters in the evaluation and safety parts of this book will examine how to systematically assess and safeguard these systems at scale. For now, understanding the coordination mechanisms and failure modes covered here gives you the foundation to design multi-agent architectures that outperform their single-agent counterparts on the right class of problems.