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agent-design

Multi-Agent Communication Protocol Designer

Designs robust communication protocols and message schemas for multi-agent AI systems.

multi-agent communication-protocols system-architecture
prompt
# Multi-Agent Communication Protocol Design

You are an expert in distributed systems and multi-agent architectures. Design a communication protocol for the specified agent system.

## System Requirements:
```
[describe the multi-agent system - number of agents, roles, coordination needs]
```

## Agent Types and Capabilities:
```
[list each agent type with their specific functions and constraints]
```

## Communication Constraints:
- Network topology: [mesh/hub-and-spoke/hierarchical]
- Latency requirements: [real-time/near-real-time/asynchronous]
- Reliability needs: [at-least-once/exactly-once/best-effort]
- Security requirements: [authentication/encryption/access-control]

## Design Framework:

### 1. Message Schema Design
Define structured message formats for:
- Task delegation and assignment
- Status updates and progress reports
- Resource requests and allocation
- Error handling and recovery
- Coordination and consensus

### 2. Protocol Specification
- Message routing strategy
- Acknowledgement patterns
- Timeout and retry logic
- Failure detection mechanisms
- Load balancing approach

### 3. State Management
- Distributed state synchronisation
- Conflict resolution strategies
- Consistency guarantees
- Recovery procedures

### 4. Implementation Guidelines
- Recommended transport layer (gRPC/REST/WebSocket/message queue)
- Serialisation format (JSON/Protocol Buffers/MessagePack)
- Authentication and authorisation patterns
- Monitoring and observability hooks

### 5. Example Implementation
Provide code snippets showing:
- Message class definitions
- Basic send/receive patterns
- Error handling logic
- Integration points

### 6. Testing Strategy
- Unit tests for message validation
- Integration tests for protocol flows
- Chaos testing scenarios
- Performance benchmarking approach

Focus on protocols that can handle agent failures, network partitions, and scaling requirements.

Essential for building reliable multi-agent systems where agents need to coordinate, share tasks, or maintain distributed state. Particularly useful for autonomous agent swarms, distributed AI workflows, and collaborative reasoning systems. Works with Claude, GPT-4, and Gemini.