As AI agents become more capable, single-agent architectures hit scaling limits. Multi-agent systems distribute work across specialized agents, each focusing on what it does best.

Orchestration Patterns

Supervisor Pattern

A central supervisor agent delegates tasks to worker agents, collects results, and synthesizes the final output. Simple to reason about, but the supervisor becomes a bottleneck.

Hierarchical Teams

Agents are organized into teams with team leads. Each team handles a domain (research, coding, review), and leads coordinate with each other.

Swarm Pattern

Agents hand off control to each other dynamically based on context. No central coordinator — the conversation flows naturally between specialists.

Challenges

• State synchronization: How do agents share context without losing coherence?
• Error propagation: One agent’s mistake can cascade through the system
• Cost management: More agents means more LLM calls means higher costs

Best Practices

Start with a single agent. Add agents only when you can clearly define their specialty and measure their contribution. Complexity is the enemy of reliability.