custom_functions.md

Custom Function Agents

Custom Function Agents are AI agents that leverage specialized function calls (or “tools”) to perform tasks. Unlike general-purpose agents, Custom Function Agents focus on powering advanced workflows by integrating directly with your application's logic. For example, you can expose database queries, system commands, or any custom utility as isolated functions for the agent to invoke.

Why Custom Function Agents?

• Modular and Extensible: Instead of building one monolithic agent, you can design individual functions that represent discrete capabilities, making your architecture more extensible.
• Fine-Grained Control: Developers can carefully control the agent’s actions by specifying exactly which functions are available and what parameters they accept.
• Improved Reliability: By structuring each function with clear schemas and validations, you reduce errors and unexpected behaviors.

Basic Workflow

1. Identify Functions
   Determine which tasks can be transformed into custom functions (e.g., file I/O, database queries, streaming data processing).

2. Define the Interface
   Use a function signature or schema that precisely outlines each function’s inputs, outputs, and expected behavior. This enforces strong contracts between your agent and its environment.

3. Register with the Agent
   Your agent needs to “learn” which functions are available. Typically, you pass metadata describing each function’s interface to the language model or agent framework.

4. Invoke and Validate
   Once the agent selects a function to call, run the function with the provided arguments and validate the results. If valid, feed the results back to the agent for context to drive subsequent decisions.

Example

Below is a simplified example demonstrating how custom function calls might look in pseudocode. The objective is to perform a user-defined search and retrieve relevant content:

# Define a custom function with clear input/output types
def search_database(query: str) -> list:
    """
    Search the database for articles matching the query.
    
    Args:
        query (str): The search query string
        
    Returns:
        list: List of matching article results
    """
    try:
        results = database.search(query)
        return results
    except DatabaseError as e:
        logging.error(f"Database search failed: {e}")
        return []

# Register the function with the agent
agent.register_function(
    name="search_database",
    function=search_database,
    description="Searches database for articles matching a query"
)

# Example usage
def process_search():
    query = "Find recent articles on AI"
    results = agent.invoke("search_database", query)
    
    if results:
        agent.process_results(results)
    else:
        logging.info("No results found for query")

Further Reading

• smolagents Blog - Learn about the latest advancements in AI agents and how they can be applied to custom function agents.
• Building Good Agents - A comprehensive guide on best practices for developing reliable and effective custom function agents.