Multi-Agent Systems › Multi-Agent Systems

Single agents are powerful, but many real-world tasks require collaboration. A lone agent running a ReAct loop can browse the web, write code, and call APIs, but it faces a fundamental bottleneck: it works sequentially, can hold only so much context, and accumulates errors over long horizons. Multi-agent systems address this by distributing work across multiple specialized agents that communicate and coordinate while checking each other's outputs.

The intuition is familiar from human organizations. A software team has engineers, testers, product managers, and designers. Each role requires different expertise, and their parallel work is more productive than any one person handling everything. Multi-agent AI systems mirror this structure: an orchestrator decomposes tasks, assigns subtasks to specialist subagents, collects results, and synthesizes a final output. The analogy extends further: just as teams develop norms for communicating status updates, escalating blockers, and resolving disagreements, multi-agent systems need explicit protocols for exactly those situations.

This chapter covers the architectural patterns, communication protocols, and coordination mechanisms that make multi-agent systems work. Building on previous chapters about agent architectures and memory as well as planning, we now examine how multiple agents collaborate. We will look at how agents communicate through shared environments or direct message passing, how roles get assigned, how conflict gets resolved, and how to measure whether a multi-agent system performs better than a single agent. The final chapter in this part, Agent Safety, will address the additional risks that emerge when multiple autonomous agents interact.