#atom

Subtitle:

Enabling specialized AI agents to collaborate by passing control between them

Core Idea:

Agent handoffs allow a primary AI agent to delegate tasks to specialized agents, enabling a mixture of experts approach that improves task performance and reduces hallucinations through specialization.

Key Principles:

1. Specialization:
   • Each agent focuses on a specific domain or function
   • Specialized knowledge reduces errors and hallucinations
2. Contextual Delegation:
   • Primary agent determines when to hand off control based on task requirements
   • Clear handoff descriptions guide appropriate delegation
3. Seamless User Experience:
   • Handoffs appear as a unified conversation to the end user
   • Context is maintained across agent transitions

Why It Matters:

• Improved Response Quality:
  • Domain-specific agents produce more accurate responses
• Context Window Optimization:
  • Splitting responsibilities prevents overloading a single agent's context window
• Maintainability:
  • Easier to update or replace individual specialized agents than a monolithic system

How to Implement:

1. Define Specialized Agents:
   • Create agents with focused instructions and relevant tools
   • Include clear handoff descriptions
2. Configure Primary Agent:
   • Add specialized agents to the primary agent's handoff list
   • Ensure primary agent has instructions on when to delegate
3. Test Handoff Triggers:
   • Verify that user queries appropriately trigger handoffs to the right specialists

Example:

• Scenario:
  • Travel planning system with specialized flight and hotel booking agents
• Application:

# Define specialized flight agent
flight_agent = agents.Agent(
    name="FlightSpecialist",
    handoff_description="Specialized agent for finding and recommending flights",
    instructions="You are an expert in flight bookings...",
    tools=[search_flights],
    output_type=FlightRecommendation
)

# Define specialized hotel agent
hotel_agent = agents.Agent(
    name="HotelSpecialist",
    handoff_description="Specialized agent for finding and recommending hotels",
    instructions="You are an expert in hotel bookings...",
    tools=[search_hotels],
    output_type=HotelRecommendation
)

# Configure primary travel agent with handoffs
travel_agent = agents.Agent(
    name="TravelPlanner",
    instructions="Help users plan trips...",
    handoffs=[flight_agent, hotel_agent]
)

• Result:
  • When user asks "Find me a flight from New York to Chicago", the primary agent hands off to the flight specialist
  • When user asks about hotels, the hotel specialist handles the query

Connections:

• Related Concepts:
  • Agents SDK Overview: Framework that simplifies agent handoff implementation
  • AI Agent Specialization: The principle behind creating domain-specific agents
• Broader Concepts:
  • Mixture of Experts: The machine learning principle that inspired agent specialization
  • Conversational Flow Management: How to maintain coherent user experiences across handoffs

References:

1. Primary Source:
   • OpenAI Agents SDK documentation on handoffs
2. Additional Resources:
   • Research papers on mixture of experts in AI systems
   • Best practices for conversational AI architecture

Tags:

#ai #agents #handoffs #specialization #mixture-of-experts #conversation-management #collaboration

Connections:

Sources:

• From: Cole Medin - El nuevo SDK de agentes de OpenAI (curso intensivo)