Multi-Agent Architectures: When One Agent Isn't Enough

As AI agent applications grow more complex, single-agent architectures often hit limitations. Multi-agent systems — where specialized agents collaborate to solve problems — offer a powerful alternative. This article explores the key patterns and when to use each one.

The Case for Multi-Agent Systems

A single agent trying to handle research, analysis, writing, and quality assurance will inevitably produce lower quality output than a team of specialists. Multi-agent architectures mirror how human teams work: each agent has a focused role and deep expertise in its domain.

Common Multi-Agent Patterns

• Manager-Worker: A coordinator agent delegates tasks to specialized worker agents and aggregates their results
• Pipeline: Agents are chained sequentially, each processing and enriching the output of the previous agent
• Debate: Multiple agents analyze the same problem independently, then reach consensus through structured discussion
• Hierarchical: Agents are organized in a tree structure with managers overseeing teams of sub-agents

Designing Agent Communication

The most critical aspect of multi-agent systems is how agents communicate. Structured message passing with clear schemas prevents misunderstandings. Define explicit protocols for task delegation, progress reporting, and error escalation.

Shared Memory and State

Multi-agent teams need shared context to avoid redundant work and maintain consistency. Implement a shared memory layer where agents can read and write relevant state. Use locking or versioning to prevent conflicts when multiple agents update the same data.

When designed correctly, multi-agent systems can tackle problems that are simply impossible for individual agents. The key is starting simple — two agents working together — and adding complexity only when the use case demands it.