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Multi-Agent Systems

Multi-agent systems enable multiple AI agents to work together, combining their specialized skills to solve complex problems that would be difficult for a single agent to handle.

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Why Multi-Agent Systems?

Specialization

Each agent focuses on what it does best

Scalability

Distribute work across multiple agents

Resilience

System continues if one agent fails

Modularity

Easy to add, remove, or update agents

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Architecture Patterns

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Orchestrator Pattern

A central orchestrator agent coordinates multiple worker agents. Use Cases:
  • Complex research tasks
  • Multi-step workflows
  • Task delegation systems

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Peer-to-Peer Pattern

Agents communicate directly with each other without a central coordinator. Use Cases:
  • Collaborative problem-solving
  • Distributed decision-making
  • Consensus building

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Pipeline Pattern

Agents process tasks sequentially, each adding value. Use Cases:
  • Data processing pipelines
  • Content generation workflows
  • Quality assurance systems

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Communication Protocols

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Message Passing

Agents exchange structured messages using the Bindu protocol. 
{
  "jsonrpc": "2.0",
  "method": "message/send",
  "params": {
    "message": {
      "role": "agent",
      "parts": [
        {
          "kind": "text",
          "text": "I've completed the research. Here are my findings..."
        }
      ]
    }
  }
}

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Shared Context

Agents share a common context to maintain conversation continuity. 
context_id = "550e8400-e29b-41d4-a716-446655440027"

# Agent 1 creates task
agent1.send_message(context_id=context_id, task_id=task1_id)

# Agent 2 references previous task
agent2.send_message(
    context_id=context_id,
    task_id=task2_id,
    reference_task_ids=[task1_id]
)

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Coordination Strategies

Agents execute tasks one after another in a defined order.Pros: Simple, predictable Cons: Slower, no parallelism
Multiple agents work simultaneously on different subtasks.Pros: Fast, efficient Cons: Requires coordination
Route tasks to agents based on conditions or agent capabilities.Pros: Flexible, optimized Cons: Complex logic
Agents bid on tasks and negotiate terms before execution.Pros: Market-based, fair Cons: Overhead

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Example: Research Team

Here’s a multi-agent system for research tasks: 
from bindu.penguin.pebblify import pebblify
from agno.agent import Agent
from agno.models.openai import OpenAIChat

# Research Agent
@pebblify(
    name="Research Agent",
    description="Finds and collects information"
)
def research_agent(query: str) -> dict:
    agent = Agent(
        instructions="You are a research specialist. Find relevant information.",
        model=OpenAIChat(id="gpt-4o"),
        tools=[web_search, arxiv_search]
    )
    return agent.run(input=query)

# Analysis Agent
@pebblify(
    name="Analysis Agent",
    description="Analyzes and synthesizes information"
)
def analysis_agent(research_data: dict) -> dict:
    agent = Agent(
        instructions="You are an analyst. Synthesize findings.",
        model=OpenAIChat(id="gpt-4o")
    )
    return agent.run(input=research_data)

# Writing Agent
@pebblify(
    name="Writing Agent",
    description="Creates polished reports"
)
def writing_agent(analysis: dict) -> str:
    agent = Agent(
        instructions="You are a technical writer. Create clear reports.",
        model=OpenAIChat(id="gpt-4o")
    )
    return agent.run(input=analysis)

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Orchestration Example

class ResearchOrchestrator:
    def __init__(self):
        self.research_agent = research_agent
        self.analysis_agent = analysis_agent
        self.writing_agent = writing_agent
    
    async def execute(self, query: str) -> str:
        # Step 1: Research
        research_results = await self.research_agent(query)
        
        # Step 2: Analysis
        analysis = await self.analysis_agent(research_results)
        
        # Step 3: Writing
        report = await self.writing_agent(analysis)
        
        return report

# Usage
orchestrator = ResearchOrchestrator()
report = await orchestrator.execute("Latest developments in quantum computing")

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Benefits of Multi-Agent Systems

Specialization

Each agent becomes expert in its domain

Parallel Processing

Multiple tasks execute simultaneously

Fault Tolerance

System continues if individual agents fail

Scalability

Add more agents as workload increases

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Challenges

Multi-agent systems introduce complexity. Start simple and add agents as needed.
  • Coordination Overhead: Managing agent communication and synchronization
  • Conflict Resolution: Handling disagreements between agents
  • Resource Management: Balancing load across agents
  • Debugging: Tracing issues across multiple agents
  • Cost: Running multiple AI models simultaneously

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Best Practices

  1. Start Simple: Begin with 2-3 agents before scaling up
  2. Clear Roles: Define specific responsibilities for each agent
  3. Shared Context: Use context IDs to maintain conversation continuity
  4. Error Handling: Implement fallbacks for agent failures
  5. Monitoring: Track performance and interactions between agents
  6. Testing: Test agent interactions thoroughly

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Next Steps

Protocol

Learn the Bindu protocol

API Reference

Explore API endpoints

Examples

See multi-agent examples

Playground

Try multi-agent systems
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