Multi-Agent System
An architecture in which multiple specialized AI agents collaborate — through shared memory, message passing, or an orchestrator — to solve tasks too complex or parallel for a single agent.
Definition
A multi-agent system (MAS) is a design pattern where the work of a complex task is distributed across two or more autonomous agents, each operating its own observe-reason-act loop. Agents may specialize by domain, capability, or access permissions, and coordinate through a shared protocol.
This mirrors how human organizations work: instead of one person doing everything, specialized workers collaborate under a manager.
Common Topologies
Supervisor / Worker
A supervisor agent receives the top-level goal, decomposes it into sub-tasks, and dispatches each to a specialized worker agent. Workers return results to the supervisor, which synthesizes a final response. This is the most common MAS pattern in production systems.
Peer-to-Peer
Agents communicate directly with one another without a central orchestrator. Useful for simulation, debate, and consensus-finding tasks. Harder to audit and control.
Pipeline
Agents are arranged in a directed sequence. The output of one agent is the input to the next. Useful for document processing workflows (extract → summarize → validate → format).
Blackboard
Agents read from and write to a shared memory store (the "blackboard"). Each agent monitors the state and contributes when it can add value. Loosely coupled; useful for open-ended research tasks.
Benefits
- Parallelism — independent sub-tasks can run concurrently, reducing wall-clock time.
- Specialization — agents can be optimized (different models, prompts, tools) for their specific role.
- Scalability — the system can handle larger tasks by adding more workers.
- Fault isolation — a failing sub-agent can be retried or replaced without restarting the entire workflow.
Challenges
- Coordination overhead — message passing, context serialization, and aggregation add latency and complexity.
- Context fragmentation — each agent has a partial view of the overall state; ensuring consistency is non-trivial.
- Trust between agents — one agent's output becomes another's input; prompt injection risks compound in multi-hop pipelines.
- Debugging — tracing a failure across multiple agents requires distributed tracing tooling.
Framework Support
LangGraph models MAS as an explicit graph of nodes (agents) and edges (message flow). AutoGen uses a conversational message-passing model. CrewAI provides a higher-level role-based abstraction.