Agent Communication & Coordination

Once you have multiple agents, the challenge shifts from "what can each agent do?" to "how do agents work together effectively?" This lesson covers message passing patterns, shared blackboards, handoff protocols, conflict resolution, and structured inter-agent communication.

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The Communication Challenge

In a multi-agent system, agents must:

1. Share information — Pass findings, results, and context between agents.
2. Coordinate actions — Avoid duplicating work or conflicting with each other.
3. Resolve disagreements — Handle cases where agents produce contradictory results.
4. Maintain coherence — Ensure the final output is consistent, not a disconnected patchwork.

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Message Passing

The simplest coordination mechanism: agents send structured messages to each other.

Structured Message Format

from dataclasses import dataclass, field
from datetime import datetime
from typing import Any

@dataclass
class AgentMessage:
    sender: str
    recipient: str
    message_type: str   # "request", "response", "inform", "handoff"
    content: str
    metadata: dict[str, Any] = field(default_factory=dict)
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

class MessageBus:
    """Central message bus for agent communication."""

    def __init__(self):
        self.queues: dict[str, list[AgentMessage]] = {}
        self.log: list[AgentMessage] = []

    def register_agent(self, agent_name: str):
        self.queues[agent_name] = []