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Subtitle:

Function-based capabilities that enable AI agents to interact with external systems and data sources

Core Idea:

AI agent tools are specialized functions that extend agent capabilities beyond text generation, allowing them to take actions like retrieving data, performing calculations, or interacting with external services via well-defined interfaces.

Key Principles:

1. Function Definition:
   • Tools are defined as functions with clear parameters and return values
   • Documentation strings communicate tool purpose and usage to the agent
2. Purpose-driven Design:
   • Each tool serves a specific, well-defined purpose within the agent system
   • Tools should encapsulate complex operations behind simple interfaces
3. Parameter Control:
   • LLMs determine when to call tools and what parameters to use
   • Tools validate inputs to prevent misuse or errors

Why It Matters:

• Agent Capability Extension:
  • Expands what agents can do beyond their pre-trained knowledge
• Real-time Data Access:
  • Provides current information that may be outside the LLM's training data
• Action Execution:
  • Enables agents to effect changes in external systems rather than just generate text

How to Implement:

1. Define Tool Function:
   • Create a Python function with appropriate parameters
   • Add comprehensive docstring explaining tool usage
2. Apply Framework Decorators:
   • Use framework-specific decorators to register as a tool
   • Configure parameter validation and error handling
3. Integrate with Agent:
   • Add tool to agent's available tools list
   • Ensure agent instructions explain when to use the tool

Example:

• Scenario:
  • Creating a weather forecasting tool for a travel planning agent
• Application:

from openai import agents

@agents.tool
def get_weather_forecast(city: str, date: str) -> str:
    """
    Get the weather forecast for a specific city and date.
    Use this tool when the user asks about weather conditions
    for their travel destination.
    
    Parameters:
    - city: The name of the city to get weather for
    - date: The date in YYYY-MM-DD format
    
    Returns:
    - A string describing the weather forecast
    """
    # In a real implementation, this would call a weather API
    weather_data = {
        "Miami": "Sunny, 85°F (29°C)",
        "Tokyo": "Partly cloudy, 68°F (20°C)",
        "Paris": "Light rain, 59°F (15°C)"
    }
    
    if city in weather_data:
        return f"Weather forecast for {city} on {date}: {weather_data[city]}"
    else:
        return f"Weather forecast not available for {city}"

# Add tool to agent
travel_agent = agents.Agent(
    name="TravelPlanner",
    instructions="Help users plan trips...",
    tools=[get_weather_forecast]
)

• Result:
  • When a user asks "What's the weather like in Tokyo for my trip next week?", the agent:
    1. Identifies the need for weather information
    2. Calls the get_weather_forecast tool with appropriate parameters
    3. Integrates the returned forecast into its response

Connections:

• Related Concepts:
  • Agents SDK Overview: Framework that simplifies tool implementation
  • Agent Specialization: How specialized agents might use different tools
• Broader Concepts:
  • Function Calling in LLMs: The underlying capability that enables tool use
  • API Integration Patterns: Methods for connecting tools to external services

References:

1. Primary Source:
   • OpenAI documentation on function calling and tools
2. Additional Resources:
   • Research papers on tool use in LLM-based agents
   • Best practices for tool design and implementation

Tags:

#ai #agents #tools #function-calling #external-capabilities #integration

Connections:

Sources:

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