Multi-Agent Communication Flows
📚 Complete Communication Documentation
Detailed multi-agent communication patterns are covered in Multi-Agent Orchestration.
For implementation details, see Visual Orchestration.
🔄 Communication Patterns
Sequential Communication
Agents pass tasks in a linear sequence, with each agent building on the previous agent's work:
Agent 1 → Agent 2 → Agent 3 → Final OutputUse Cases:
Document Processing: OCR → Analysis → Summary → Formatting
Customer Support: Intake → Technical → Resolution → Follow-up
Content Creation: Research → Writing → Editing → Publishing
Benefits:
Clear Chain of Responsibility: Each agent has a specific role
Quality Control: Each step can validate previous work
Specialization: Agents become experts in their domain
Parallel Communication
Multiple agents work simultaneously on different aspects of the same task:
Agent B
/ \
Input Coordinator → Output
\ /
Agent CUse Cases:
Research Projects: Multiple agents research different aspects simultaneously
Data Analysis: Different agents analyze different data sets
Content Generation: Simultaneous research, writing, and design
Benefits:
Speed: Multiple agents work simultaneously
Diverse Perspectives: Different agents bring different expertise
Redundancy: Backup if one agent encounters issues
Hierarchical Communication
Supervisor agents coordinate and direct specialist agents:
Supervisor Agent
├── Specialist A
├── Specialist B
└── Specialist CUse Cases:
Project Management: Supervisor delegates tasks to specialists
Quality Assurance: Supervisor reviews and approves specialist work
Resource Allocation: Supervisor optimizes agent assignments
Benefits:
Coordination: Clear oversight and task management
Optimization: Supervisor can balance workloads
Quality Control: Centralized review and approval
💬 Communication Mechanisms
Message Passing
Agents communicate through structured message formats:
{
"from": "agent_researcher",
"to": "agent_writer",
"task_id": "article_001",
"message_type": "research_complete",
"data": {
"topic": "AI automation trends",
"key_findings": ["trend1", "trend2", "trend3"],
"sources": ["url1", "url2", "url3"],
"confidence": 0.92
},
"timestamp": "2025-08-25T10:30:00Z"
}Shared Context
Agents maintain shared understanding through:
Task Context: Shared information about the current task
User Context: Information about the user and their preferences
Conversation History: Previous interactions and decisions
Knowledge Base: Shared access to relevant information
Event-Driven Communication
Agents respond to events and triggers:
Task Completion: Agent notifies others when work is finished
Error Events: Agents alert team when issues arise
Status Updates: Regular progress reports between agents
External Triggers: Events from external systems or users
🎯 Communication Protocols
Handoff Protocols
Structured processes for transferring tasks between agents:
Task Preparation: Current agent packages work and context
Handoff Request: Agent requests task transfer to specialist
Acceptance Confirmation: Receiving agent confirms capability
Context Transfer: Complete information and history shared
Handoff Complete: Original agent confirms successful transfer
Escalation Protocols
Procedures for handling complex or problematic tasks:
Issue Detection: Agent identifies task beyond capabilities
Escalation Request: Agent requests help from supervisor or specialist
Context Preservation: All work and context maintained
Expert Assignment: Most qualified agent takes over task
Resolution Tracking: Progress monitored until completion
Quality Assurance Protocols
Communication patterns for maintaining output quality:
Work Completion: Agent completes assigned task
Quality Review: Designated reviewer checks output
Feedback Communication: Reviewer provides specific feedback
Revision Process: Original agent makes necessary improvements
Approval Confirmation: Final approval and task completion
🔧 Advanced Communication Features
Context Awareness
Agents maintain awareness of:
Team State: What other agents are working on
User Preferences: Individual user needs and preferences
Business Rules: Company policies and procedures
Performance Metrics: Team efficiency and success rates
Adaptive Routing
Smart communication routing based on:
Agent Availability: Current workload and capacity
Expertise Match: Best agent for specific task types
Performance History: Success rates with similar tasks
User Preferences: Preferred agents or communication styles
Communication Optimization
Continuous improvement of team communication:
Pattern Recognition: Identify common communication flows
Bottleneck Detection: Find and resolve communication delays
Efficiency Metrics: Track communication overhead and success
Automated Optimization: AI-driven improvements to communication patterns
📊 Communication Analytics
Flow Analysis
Track how information moves through your team:
Message Volume: Frequency of inter-agent communication
Response Times: Speed of agent-to-agent responses
Information Quality: Completeness and accuracy of passed data
Communication Efficiency: Ratio of successful to failed handoffs
Collaboration Metrics
Measure team collaboration effectiveness:
Handoff Success Rate: Percentage of smooth task transfers
Escalation Frequency: How often agents need help
Context Preservation: Quality of information transfer
Team Coordination: Overall team synchronization
Performance Impact
Understand how communication affects results:
Task Completion Speed: Impact of communication on delivery time
Output Quality: Correlation between communication and results
User Satisfaction: How team communication affects user experience
Resource Utilization: Communication overhead vs. productive work
🛠️ Setting Up Communication Flows
Basic Flow Configuration
Define Roles: Specify what each agent does
Map Interactions: Identify when agents need to communicate
Set Protocols: Establish communication standards
Test Flows: Validate communication patterns work correctly
Advanced Configuration
Conditional Logic: Set up if/then communication rules
Priority Handling: Define urgent communication channels
Fallback Procedures: Backup communication when agents unavailable
Integration Points: Connect to external systems and notifications
Monitoring and Optimization
Track Metrics: Monitor communication effectiveness
Identify Issues: Find communication bottlenecks
Optimize Flows: Improve based on performance data
Scale Communication: Adjust as team grows
🎯 Communication Best Practices
Clear Protocols
Standardized Formats: Consistent message structures
Defined Responsibilities: Clear role boundaries
Escalation Paths: Known procedures for complex issues
Documentation: Well-documented communication procedures
Efficient Information Transfer
Minimal Overhead: Share only necessary information
Rich Context: Include sufficient detail for good decisions
Structured Data: Use consistent data formats
Version Control: Track information changes and updates
Quality Assurance
Communication Testing: Validate all communication paths
Error Handling: Graceful handling of communication failures
Backup Procedures: Alternative communication methods
Regular Reviews: Periodic assessment of communication effectiveness
📚 Related Documentation
Multi-Agent Orchestration - Detailed orchestration patterns
Visual Orchestration - Visual design and monitoring
Team Templates - Pre-configured communication patterns
Advanced Features - Enterprise communication capabilities
Multi-agent communication is the foundation of effective AI teams. Well-designed communication flows enable agents to collaborate seamlessly, share context efficiently, and deliver superior results through coordinated teamwork.
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