Jan 2025 • 10 min read
Multi-Agent Systems: Coordinating Multiple AI Agents
Designing systems where multiple AI agents work together to solve complex problems through coordination and collaboration.
Beyond Single Agents
While single AI agents are powerful, some problems are simply too complex for one agent to handle alone. Multi-agent systems (MAS) represent the next evolution in AI applications, where multiple specialized agents work together, each bringing unique capabilities to solve problems that would overwhelm a single agent.
Think of it like a software development team: you don't have one person doing frontend, backend, database, and DevOps. You have specialists who coordinate through well-defined communication patterns. Multi-agent systems apply this same principle to AI.
Communication Architectures
The foundation of any multi-agent system is how agents communicate. In 2025, four primary patterns have emerged:
1. Hierarchical Communication
Agents are organized hierarchically with distinct roles at each level. Agents mainly interact within their layer or with adjacent layers.
Example: A manager agent coordinates multiple specialist agents, each handling specific subtasks.
Best for: Clear command structures, complex workflows with well-defined stages.
2. Decentralized Communication
Agents communicate peer-to-peer without a central coordinator. Common in world simulation tasks where direct agent interaction is key.
Example: Swarm intelligence systems where agents share discoveries directly with peers.
Best for: Emergent behaviors, distributed problem-solving, resilient systems.
3. Centralized Communication
A central agent acts as a hub, coordinating communication among all agents.
Example: An orchestrator agent that dispatches tasks and aggregates results.
Best for: Controlled workflows, easier debugging, centralized monitoring.
4. Shared Message Pool
Agents publish messages to a shared pool and subscribe to those relevant to their roles, improving communication efficiency.
Example: Event-driven systems where agents react to published events.
Best for: Scalable systems, loose coupling, event-driven architectures.
Coordination Patterns
How agents coordinate their work is just as important as how they communicate. Four key patterns dominate in 2025:
Orchestrator-Worker Pattern
A lead agent coordinates the process while delegating to specialized subagents that operate in parallel. The orchestrator maintains overall context and decides which agents to activate and when.
- Strengths: Clear control flow, easy to debug, predictable behavior
- Challenges: Single point of failure, orchestrator complexity
Swarm Pattern
Peer agents work together, exchanging information directly and iteratively. Decentralized coordination happens through shared memory or message space.
- Strengths: Resilient to individual agent failures, emergent intelligence
- Challenges: Harder to predict, potential for communication overhead
Blackboard Pattern
A shared knowledge base (the "blackboard") where agents post findings and read others' contributions. Agents work independently but coordinate through shared state.
- Strengths: Flexible agent composition, easy to add new specialists
- Challenges: Managing shared state, potential conflicts
Agent Graph
Each node is an agent with a well-defined role, and each edge represents a communication or handoff channel. This enforces precise control over the sequence and direction of inter-agent interactions.
- Strengths: Visual design, precise control, clear dependencies
- Challenges: Can become complex with many agents
Emerging Protocols in 2025
The multi-agent ecosystem has matured with standardized protocols:
MCP (Model Context Protocol)
Provides a standardized interface for accessing tools and resources. Agents can discover and use tools through a common protocol, making integration seamless.
A2A (Agent-to-Agent)
Announced in May 2025, A2A facilitates structured inter-agent communication for exchanging messages and distributing subtasks. It provides standardized primitives for agent collaboration.
Real-World Applications
Research Systems
Anthropic built a multi-agent research system where specialized agents handle different aspects of research: one agent searches for papers, another reads and summarizes, a third synthesizes findings, and a fourth critiques the conclusions.
Software Development
Multi-agent systems excel at software development tasks: one agent handles requirements analysis, another writes code, a third reviews and tests, and a fourth handles documentation. Each specialist agent is better than a generalist at its specific role.
Customer Service
Routing agents direct inquiries to specialist agents (billing, technical support, returns), with an orchestrator managing the overall conversation flow and escalation logic.
Challenges and Solutions
Challenge: Communication Breakdowns
Without proper architecture and clear communication protocols, coordinating tasks between agents becomes difficult.
Solution: Use standardized protocols like MCP and A2A, implement message validation, and add retry logic with exponential backoff.
Challenge: Unpredictable Behavior
Multiple autonomous agents can produce unexpected emergent behaviors that are difficult to predict or debug.
Solution: Implement comprehensive logging, use agent graphs for predictable flows, add human-in-the-loop checkpoints, and thoroughly test agent interactions.
Challenge: Cost Management
Multiple agents making LLM calls can quickly become expensive.
Solution: Use cheaper models for simple tasks, implement intelligent caching, set budget limits per conversation, and monitor costs carefully.
Best Practices
- Start Simple: Begin with 2-3 agents before scaling to larger systems
- Clear Responsibilities: Each agent should have a well-defined role and scope
- Observability: Implement comprehensive logging and tracing from day one
- Failure Handling: Design for graceful degradation when agents fail
- Testing: Test agent interactions thoroughly, not just individual agents
- Documentation: Document communication protocols and coordination patterns
The Future of Multi-Agent Systems
Multi-agent systems represent a paradigm shift in how we build AI applications. Instead of trying to create one superintelligent agent, we're learning to coordinate multiple specialized agents, each excellent at specific tasks.
This mirrors how humans solve complex problems: through team collaboration, division of labor, and specialized expertise. As LLMs become more capable and protocols like MCP and A2A mature, multi-agent systems will become the standard architecture for sophisticated AI applications.
The key is starting with clear communication patterns, well-defined coordination protocols, and comprehensive observability. Master these fundamentals, and you'll be building systems where the whole is truly greater than the sum of its parts.
Sources
This article was generated with the assistance of AI technology and reviewed for accuracy and relevance.