[Multi-Agent] Communication Platform Design

Sat, 21 Feb 2026 21:00:00 +0900

Introduction

As AI agent systems evolve, the transition from single-agent to multi-agent systems is accelerating. This article explores how to design a platform architecture for efficient communication between multiple agents.

1. Architecture Overview

A multi-agent platform consists primarily of an Orchestrator, Message Bus, and various specialized agents.

Multi-Agent Architecture

Core Components

Component	Role
Orchestrator	Task coordination, agent scheduling
Message Bus	Inter-agent communication relay
State Store	Shared state management
Task Queue	Priority-based work queue
Agents	Specialized task execution

2. Communication Protocol Design

Inter-agent communication is designed based on asynchronous message passing.

Message Protocol

Communication Flow

Publish: Agent publishes task request to message bus
Route: Bus routes message to appropriate agent
Accept: Target agent accepts task
Acknowledge: Bus sends acceptance confirmation to requester
Execute: Agent performs actual work
Result: Result is published to bus
Deliver: Bus delivers result to requester

3. Message Structure

Design scalable and type-safe message structures.

Message Structure

Message Envelope

{
 "header": {
 "message_id": "uuid-v4",
 "timestamp": "2026-02-21T12:00:00Z",
 "source_agent": "agent-research-01",
 "target_agent": "agent-writer-01",
 "message_type": "TASK_REQUEST"
 },
 "metadata": {
 "priority": "normal",
 "ttl": 3600,
 "correlation_id": "parent-uuid"
 },
 "payload": {
 "content_type": "json",
 "encoding": "utf-8",
 "data": {
 "task": "summarize",
 "content": "..."
 }
 }
}

Message Types

Type	Purpose
`TASK_REQUEST`	Task request
`TASK_RESULT`	Task result
`STATUS_UPDATE`	Status change notification
`HEARTBEAT`	Liveness check
`QUERY`	Information lookup
`RESPONSE`	Query response

4. Agent State Management

Each agent has a clear state machine.

State Management

State Transitions

IDLE → RECEIVING → PROCESSING → EXECUTING → COMPLETED → IDLE
 ↘ ↘ ↘
 ERROR ──────→ retry ──→ IDLE

State Behaviors

State	Behavior
IDLE	Waiting for new task
RECEIVING	Message reception and validation
PROCESSING	Task analysis and preparation
EXECUTING	Actual work execution
COMPLETED	Result transmission and cleanup
ERROR	Error handling and retry

5. Communication Patterns

5.1 Request-Response

Synchronous 1:1 communication pattern.

# Request
response = await bus.request(
 target="agent-code-01",
 message=TaskRequest(task="generate_code", spec={...}),
 timeout=30
)

# Response
return TaskResult(status="success", data=generated_code)

Asynchronous 1:N communication pattern.

# Subscribe
@bus.subscribe("research.completed")
async def handle_research_result(message):
 # Handle research result
 pass

# Publish
await bus.publish(
 topic="research.completed",
 message=ResearchResult(data={...})
)

5.3 Broadcast

Used to send notifications to all agents.

# Broadcast
await bus.broadcast(
 message=SystemAlert(
 level="warning",
 message="High load detected"
 )
)

6. Core Design Principles

6.1 Loose Coupling

Agents should not know each other’s internal implementations. They communicate only through message interfaces.

6.2 Fault Tolerance

class Agent:
 async def execute_with_retry(self, task, max_retries=3):
 for attempt in range(max_retries):
 try:
 return await self.execute(task)
 except Exception as e:
 if attempt == max_retries - 1:
 await self.transition_to(State.ERROR)
 raise
 await asyncio.sleep(2 ** attempt) # Exponential backoff

6.3 Scalability

Horizontal scaling: Add agents with the same role
Load balancing: Automatic distribution by message bus
Sharding: Separate agent groups by task type

6.4 Observability

# Metrics collection
@metrics.track
async def process_message(self, message):
 with metrics.timer("process_duration"):
 result = await self.handle(message)
 metrics.increment("messages_processed")
 return result

7. Implementation Considerations

7.1 Message Serialization

Format	Pros	Cons
JSON	Readability, compatibility	Size, performance
Protocol Buffers	Performance, schema	Complexity
MessagePack	JSON compatible, size	Tool support

7.2 Backpressure

class Agent:
 def __init__(self, max_concurrent=10):
 self.semaphore = asyncio.Semaphore(max_concurrent)

 async def process(self, message):
 async with self.semaphore:
 return await self.handle(message)

7.3 Dead Letter Queue

Failed messages are moved to a separate queue for analysis and reprocessing.

8. Conclusion

Key elements of a multi-agent communication platform include:

Flexible message structure: Support for various communication scenarios
Clear state management: Predictable agent behavior
Diverse communication patterns: Request-response, publish-subscribe, broadcast
Fault-tolerant design: Retry, timeout, error handling

Based on these principles, scalable and reliable multi-agent systems can be built.

References

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