Multi-Agent System Architecture: How to Build When One Agent Isn't Enough
Complex tasks require multiple cooperating agents. Here's how leading teams are architecting multi-agent systems, the patterns that work, and the failure modes to avoid.
Single-agent systems hit natural limits. Context windows fill. Long-horizon tasks exceed reliable planning horizons. Some tasks genuinely benefit from parallel execution or specialized expertise.
Why Multi-Agent
Parallelism: Divide a large task into parallel subtasks. A research agent spawning five sub-agents to investigate different aspects can compress a 30-minute sequential task into 5 minutes.
Specialization: A code review agent fine-tuned for code is more reliable than a generalist agent. Multi-agent systems route tasks to specialized agents.
Context management: Hierarchical agent architectures with an orchestrator delegating to subagents sidestep context window limits by keeping each agent focused.
Core Architectural Patterns
Supervisor + Worker: One orchestrator decomposes tasks, assigns to workers, collects outputs, synthesizes results. Simple and predictable for most use cases.
Pipeline: Agents in fixed sequence, each taking the previous output as input. Works well for multi-stage processes with defined order (plan → research → draft → review).
Peer-to-peer: Agents communicate directly, negotiating tasks. More flexible but significantly harder to debug.
Frameworks
LangGraph (from LangChain) provides a graph-based abstraction for multi-agent state machines. Explicit state management is its key advantage.
Microsoft AutoGen is designed specifically for multi-agent conversation patterns with its conversable agent abstraction.
crewAI is optimized for role-based workflows — you define agents with specific roles that shape their behavior.
The Failure Modes
Agent loops, context fragmentation between agents, and cost explosions from missing termination conditions are the most common. Always implement hard token budget limits and explicit stopping conditions.
