#atom

Subtitle:

Applying structured formats to enhance reliability and functionality in multi-agent systems

Core Idea:

Structured outputs in AI agent systems extend the concept of general LLM structured generation by standardizing the communication format between specialized agents, tools, and handoff mechanisms, creating consistent patterns for complex agent ecosystems.

Key Principles:

1. Agent Response Standardization:
   • Each specialized agent produces outputs in a consistent, predictable structure
   • Defines exactly what information types must be included in agent responses
2. Cross-Agent Compatibility:
   • Ensures information can seamlessly flow between different specialized agents
   • Provides common data structures for agent handoffs and collaboration
3. Tool Result Integration:
   • Standardizes how tool results are incorporated into agent responses
   • Maintains structure integrity throughout multi-step agent processes

Why It Matters:

• Agent Specialization Support:
  • Enables clear interfaces between different specialized agents in a system
• Reduced Hallucinations:
  • Constrains agent outputs to expected formats, limiting opportunity for fabrication
• Quality Control:
  • Makes it easier to validate agent responses against expected patterns

How to Implement:

1. Define Agent-Specific Schemas:
   • Create data classes or models for each agent type's output format
   • Include all required fields and relationships
2. Register Output Types:
   • Configure agents with their expected output structures
   • Set up validation to ensure compliance
3. Implement Inter-Agent Interfaces:
   • Design how structured data flows between agents during handoffs
   • Create transformation functions where needed

Example:

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

from dataclasses import dataclass
from typing import List, Optional
from openai import agents

# Define structured output for flight agent
@dataclass
class FlightRecommendation:
    airline: str
    departure_time: str
    arrival_time: str
    price: str
    direct_flight: bool
    justification: str

# Define structured output for hotel agent
@dataclass
class HotelRecommendation:
    hotel_name: str
    location: str
    price_per_night: str
    available_amenities: List[str]
    distance_from_center: str
    justification: str

# Configure specialized agents with their output types
flight_agent = agents.Agent(
    name="FlightSpecialist",
    instructions="Find optimal flights...",
    tools=[search_flights],
    output_type=FlightRecommendation
)

hotel_agent = agents.Agent(
    name="HotelSpecialist",
    instructions="Find suitable hotels...",
    tools=[search_hotels],
    output_type=HotelRecommendation
)

• Result:
  • When the flight agent is called, it always returns a complete FlightRecommendation
  • When the hotel agent is called, it always returns a complete HotelRecommendation
  • Main agent can predictably access fields like flight_result.price or hotel_result.amenities

Connections:

• Related Concepts:
  • Structured Outputs in LLMs: Foundational concept that agent-specific structured outputs build upon
  • Agent Handoffs: Process facilitated by structured output standardization
• Broader Concepts:
  • API Design Patterns: Similar principles of interface standardization
  • Multi-Agent System Architecture: Framework for organizing agent collaboration

References:

1. Primary Source:
   • OpenAI Agents SDK documentation on structured outputs
2. Additional Resources:
   • Agent design patterns for consistent data flow
   • Schema validation techniques for agent outputs

Tags:

#ai #agents #structured-data #integration #agent-communication #standardization #data-validation

Connections:

Sources:

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