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Multi-Agent
Coordination and communication patterns for multiple AI agents
Overview
Multi-agent patterns enable sophisticated coordination, collaboration, and communication between multiple specialized AI agents working together to solve complex problems beyond individual agent capabilities. These patterns implement advanced orchestration mechanisms including supervisor-worker architectures, shared workspace collaboration, sequential pipelines, concurrent processing, and intelligent handoff systems. Modern multi-agent systems leverage state-of-the-art frameworks like LangGraph, Google ADK, and CrewAI to create enterprise-grade agentic workflows with proper error handling, scalability, and performance optimization. Recent research demonstrates that well-designed multi-agent systems can achieve 90% performance improvements over single-agent approaches for complex, multi-domain tasks.
Practical Applications & Use Cases
Enterprise Research Systems
Supervisor-worker architectures coordinating specialized research agents for comprehensive analysis with 90% performance improvements over single agents.
Complex Content Creation
Sequential pipelines where research, writing, editing, and fact-checking agents collaborate in structured workflows with quality checkpoints.
Parallel Decision Analysis
Concurrent orchestration where multiple specialist agents (financial, market, risk, technical) simultaneously analyze investment decisions from different perspectives.
Dynamic Customer Support
Handoff orchestration intelligently routing customer queries to appropriate specialist agents based on context and complexity.
Collaborative Document Development
Shared scratchpad systems where multiple agents transparently collaborate on documents with real-time visibility and iterative refinement.
Distributed Problem Solving
Peer collaboration networks where agents share information, negotiate resources, and reach consensus through structured communication protocols.
Real-time Information Systems
Event-driven communication protocols enabling scalable agent coordination across enterprise systems.
Cross-platform Integration
A2A protocol implementations enabling seamless collaboration between agents across different platforms and vendors.
Why This Matters
Multi-agent patterns enable the creation of sophisticated AI systems that leverage specialized capabilities and distributed processing. They allow for better scalability, improved reliability through redundancy, and enhanced problem-solving through diverse perspectives. These patterns are essential for complex applications that benefit from division of labor, specialized expertise, or require processing at scale beyond single-agent capabilities.
Implementation Guide
When to Use
Complex problems benefiting from specialized expertise or diverse perspectives
High-volume applications requiring distributed processing capabilities
Tasks where validation and peer review improve quality significantly
Scenarios requiring different roles or personas for comprehensive coverage
Applications needing redundancy and fault tolerance through multiple agents
Systems where agent specialization provides significant efficiency gains
Best Practices
Design clear communication protocols and message formats between agents
Implement proper coordination mechanisms to prevent conflicts and deadlocks
Define clear roles and responsibilities for each agent in the system
Use effective load balancing and task distribution strategies
Implement monitoring and health checks for all agents in the system
Design graceful degradation when individual agents fail or become unavailable
Establish clear decision-making and conflict resolution procedures
Common Pitfalls
Over-complicating coordination leading to communication overhead and latency
Poor task distribution causing bottlenecks or idle agents
Insufficient error handling for agent failures and communication issues
Creating dependencies that make the system fragile to individual agent failures
Not properly managing shared resources and potential conflicts between agents
Inadequate monitoring making it difficult to diagnose multi-agent system issues
Available Techniques
Multi-Agent
Coordination and communication patterns for multiple AI agents
Overview
Multi-agent patterns enable sophisticated coordination, collaboration, and communication between multiple specialized AI agents working together to solve complex problems beyond individual agent capabilities. These patterns implement advanced orchestration mechanisms including supervisor-worker architectures, shared workspace collaboration, sequential pipelines, concurrent processing, and intelligent handoff systems. Modern multi-agent systems leverage state-of-the-art frameworks like LangGraph, Google ADK, and CrewAI to create enterprise-grade agentic workflows with proper error handling, scalability, and performance optimization. Recent research demonstrates that well-designed multi-agent systems can achieve 90% performance improvements over single-agent approaches for complex, multi-domain tasks.
Practical Applications & Use Cases
Enterprise Research Systems
Supervisor-worker architectures coordinating specialized research agents for comprehensive analysis with 90% performance improvements over single agents.
Complex Content Creation
Sequential pipelines where research, writing, editing, and fact-checking agents collaborate in structured workflows with quality checkpoints.
Parallel Decision Analysis
Concurrent orchestration where multiple specialist agents (financial, market, risk, technical) simultaneously analyze investment decisions from different perspectives.
Dynamic Customer Support
Handoff orchestration intelligently routing customer queries to appropriate specialist agents based on context and complexity.
Collaborative Document Development
Shared scratchpad systems where multiple agents transparently collaborate on documents with real-time visibility and iterative refinement.
Distributed Problem Solving
Peer collaboration networks where agents share information, negotiate resources, and reach consensus through structured communication protocols.
Real-time Information Systems
Event-driven communication protocols enabling scalable agent coordination across enterprise systems.
Cross-platform Integration
A2A protocol implementations enabling seamless collaboration between agents across different platforms and vendors.
Why This Matters
Multi-agent patterns enable the creation of sophisticated AI systems that leverage specialized capabilities and distributed processing. They allow for better scalability, improved reliability through redundancy, and enhanced problem-solving through diverse perspectives. These patterns are essential for complex applications that benefit from division of labor, specialized expertise, or require processing at scale beyond single-agent capabilities.
Implementation Guide
When to Use
Complex problems benefiting from specialized expertise or diverse perspectives
High-volume applications requiring distributed processing capabilities
Tasks where validation and peer review improve quality significantly
Scenarios requiring different roles or personas for comprehensive coverage
Applications needing redundancy and fault tolerance through multiple agents
Systems where agent specialization provides significant efficiency gains
Best Practices
Design clear communication protocols and message formats between agents
Implement proper coordination mechanisms to prevent conflicts and deadlocks
Define clear roles and responsibilities for each agent in the system
Use effective load balancing and task distribution strategies
Implement monitoring and health checks for all agents in the system
Design graceful degradation when individual agents fail or become unavailable
Establish clear decision-making and conflict resolution procedures
Common Pitfalls
Over-complicating coordination leading to communication overhead and latency
Poor task distribution causing bottlenecks or idle agents
Insufficient error handling for agent failures and communication issues
Creating dependencies that make the system fragile to individual agent failures
Not properly managing shared resources and potential conflicts between agents
Inadequate monitoring making it difficult to diagnose multi-agent system issues