Integrate custom tools into your agent | AI-103 | Episode 8

AI agents become much more powerful when they can act on external systems, not just generate answers. Custom tools let a Foundry agent use application logic, databases, APIs, calculations, and workflows.

The Core Tool-Calling Pattern

Prompt → Agent → function_call → App executes tool → Result → Agent → Answer

A custom function tool has a name, description, and parameters. The agent uses these definitions to determine when a function is needed and what arguments to provide.

# 1. Create the agent
agent = project_client.agents.create_version(...)

# 2. Ask the agent
response = openai_client.responses.create(
    conversation=conversation.id,
    input="What's the weather in Zurich?",
    extra_body={"agent": agent}
)

# 3. Check whether the agent wants to use a function
for item in response.output:
    if item.type == "function_call":

        # YOUR application executes the function
        result = call_function(item.name, item.arguments)

        # Return the result to the agent
        send_function_result(item.call_id, result)

Key concept: the LLM does not execute your local function. It returns a function_call containing the requested function and arguments. Your application dispatches and executes it, then returns the result so the agent can continue reasoning.

Choose the Right Tool

Remember

Agent = decides what to call → Application = executes it → Agent = uses the result

One prompt can trigger multiple function calls, allowing an agent to combine several operations before generating its final response.