Multi-Agent Systems
Architectures where multiple specialized AI agents collaborate, compete, or coordinate to solve complex problems that exceed a single agent's capability.
What it is
A multi-agent system is an architecture where multiple specialized AI agents work together to solve complex problems. Each agent has a specific role, tools, and knowledge, and they coordinate through defined communication patterns.
The idea isn't new — it comes from distributed systems research in the 90s — but LLMs have revitalized it by making agents that communicate in natural language possible.
Coordination topologies
Centralized orchestrator
A main agent delegates tasks to specialized agents:
Orchestrator
├── Research agent
├── Code agent
└── Review agent
Advantage: clear control. Disadvantage: bottleneck at the orchestrator.
Swarm
Autonomous agents that pass control to each other based on rules:
Agent A → (condition) → Agent B → (condition) → Agent C
Advantage: flexible, decentralized. Disadvantage: hard to debug.
Debate/Competition
Multiple agents propose solutions and a judge selects the best:
Agent 1 → Proposal
Agent 2 → Proposal → Judge → Best solution
Agent 3 → Proposal
Agent graph
Agents connected in a directed graph with conditional flows:
Input → Classifier → [Route A: Agent 1 → Agent 2]
→ [Route B: Agent 3]
→ Synthesizer → Output
Frameworks
| Framework | Focus |
|---|---|
| Strands Agents | Agents-as-tools, graph, swarm |
| CrewAI | Structured roles and tasks |
| AutoGen | Multi-agent conversations |
| LangGraph | State graphs with agents |
Challenges
- Coordination: preventing agents from contradicting or duplicating work
- Cost: N agents × M iterations = many tokens
- Debugging: tracing decisions across multiple agents
- Consistency: maintaining shared context between agents
- Evaluation: measuring system performance, not just individual agents
When to use multi-agent
- The task requires expertise across multiple domains
- A single agent can't maintain all necessary context
- Checks and balances are needed (one agent reviews another)
- The problem naturally decomposes into independent subtasks
Why it matters
Multi-agent systems allow decomposing complex tasks into specialized subtasks, where each agent has its own context, tools, and model. It is the pattern that scales AI capabilities beyond what a single agent can handle.
References
- Communicative Agents for Software Development — Qian et al., 2023. ChatDev: agents collaborating to develop software.
- Agent-to-Agent Protocol — Google, 2025. Open protocol for inter-agent communication.
- AutoGen: Enabling Next-Gen LLM Applications — Wu et al., 2023. Microsoft framework for multi-agent systems.