The Next Generation of Multi-Agent AI: A Scalable Pub/Sub Architecture

1. The Sequential Bottleneck

Current systems process agent interactions sequentially. When Agent A needs 5 seconds to think, Agents B, C, and D wait idle. With 10 agents in a chain, simple tasks take minutes instead of seconds.

2. The Orchestration Complexity

Traditional architectures require complex orchestration logic:

# The old way - brittle and unmaintainable
result1 = agent1.process(task)
if result1.success:
    result2 = agent2.process(result1)
    result3 = agent3.process(result1)
    final = agent4.combine(result2, result3)

3. The Scaling Ceiling

Point-to-point connections create an n² problem. 10 agents need 45 connections. 100 agents need 4,950. The system becomes unmaintainable.

4. The Context Loss Problem

Passing context between agents leads to information loss, duplication, and inconsistency. Each handoff risks dropping critical information.

1. Install Dependencies

pip install nats-py pydantic openai anthropic

2. Create Your First Agent

import asyncio
from nats.aio.client import Client as NATS
from pydantic import BaseModel
from typing import Optional, List, Dict, Any
import json
import uuid
from datetime import datetime

class AgentMessage(BaseModel):
    id: str
    timestamp: float
    version: str = "1.0"
    type: str
    source: Dict[str, str]
    target: Optional[str] = None
    replyTo: Optional[str] = None
    correlationId: Optional[str] = None
    context: Dict[str, Any]
    payload: Dict[str, Any]
    routing: Dict[str, Any]

class BaseAgent:
    def __init__(self, agent_id: str, capabilities: List[str]):
        self.agent_id = agent_id
        self.capabilities = capabilities
        self.nats = NATS()
        
    async def connect(self, servers=["nats://localhost:4222"]):
        await self.nats.connect(servers=servers)
        
        # Subscribe to relevant topics
        for capability in self.capabilities:
            await self.nats.subscribe(
                f"agents.prod.task.{capability}.*",
                cb=self.handle_task
            )
            
        # Announce presence
        await self.announce()
        
    async def announce(self):
        message = AgentMessage(
            id=str(uuid.uuid4()),
            timestamp=datetime.now().timestamp(),
            type="event",
            source={"id": self.agent_id, "type": "agent"},
            context={"conversationId": "system"},
            payload={
                "content": {
                    "event": "agent_online",
                    "capabilities": self.capabilities
                }
            },
            routing={"priority": 0}
        )
        
        await self.nats.publish(
            "agents.prod.event.status.online",
            message.json().encode()
        )

3. Create Specialized Agents

from openai import AsyncOpenAI

class AnalysisAgent(BaseAgent):
    def __init__(self):
        super().__init__("analysis_001", ["analysis", "sentiment"])
        self.client = AsyncOpenAI()
        
    async def process_with_llm(self, message: AgentMessage) -> Dict:
        start_time = datetime.now()
        
        # Extract task content
        task = message.payload["content"]
        
        # Call LLM
        response = await self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are an expert analyst."},
                {"role": "user", "content": task["instruction"]}
            ],
            temperature=0.7
        )
        
        # Calculate metrics
        latency = (datetime.now() - start_time).total_seconds() * 1000
        
        return {
            "success": True,
            "data": response.choices[0].message.content,
            "confidence": 0.95,
            "reasoning": "Analysis completed successfully",
            "tokens": response.usage.total_tokens,
            "latency": latency
        }

1. Streaming Responses

class StreamingAgent(BaseAgent):
    async def stream_response(self, task_id: str, generator):
        sequence = 0
        
        async for chunk in generator:
            stream_msg = AgentMessage(
                id=str(uuid.uuid4()),
                timestamp=datetime.now().timestamp(),
                type="stream",
                source={"id": self.agent_id, "type": "agent"},
                correlationId=task_id,
                context={"conversationId": task_id},
                payload={
                    "sequenceNumber": sequence,
                    "isFirst": sequence == 0,
                    "isFinal": False,
                    "chunk": chunk
                },
                routing={"priority": 5}
            )
            
            await self.nats.publish(
                f"agents.prod.stream.response.{task_id}",
                stream_msg.json().encode()
            )
            sequence += 1

2. Tool Calling Integration

class ToolCallingAgent(BaseAgent):
    def __init__(self):
        super().__init__("tool_caller_001", ["tools"])
        self.tools = {}
        
    def register_tool(self, name: str, func: callable, description: str):
        self.tools[name] = {
            "function": func,
            "description": description
        }
        
    async def process_with_llm(self, message: AgentMessage) -> Dict:
        # LLM decides which tool to call
        tool_call = await self.determine_tool_call(message)
        
        if tool_call:
            # Execute tool
            result = await self.tools[tool_call["name"]]["function"](
                **tool_call["parameters"]
            )
            
            # Return tool result
            return {
                "success": True,
                "data": result,
                "tool_used": tool_call["name"]
            }

3. Context Management

class ContextManager:
    def __init__(self, max_tokens: int = 4000):
        self.max_tokens = max_tokens
        
    def compress_history(self, messages: List[Message]) -> List[Message]:
        """Intelligently compress conversation history"""
        if not messages:
            return []
            
        # Keep recent messages intact
        recent = messages[-5:]
        older = messages[:-5]
        
        if older:
            # Summarize older messages
            summary = self.summarize_messages(older)
            return [summary] + recent
        
        return recent