AI-103: Develop AI Apps and Agents on Azure

Generative AI models are powerful, but their trained knowledge is limited. Tools extend models beyond text generation, allowing them to access real-time information, take actions, ground responses in facts, extend functionality, and build intelligent workflows.

Know the Tools

Remember: current information → web_search · uploaded/private documents → file_search · calculations/code → code_interpreter · application-specific actions → function

Responses API

Tools are provided through the tools collection. The model can determine which available tool is appropriate for a request.

response = client.responses.create(
    model=model_name,
    input="Answer the user's request using the available tools.",
    tools=[
        {"type": "code_interpreter", "container": {"type": "auto"}},
        {"type": "web_search"},
        {"type": "file_search", "vector_store_ids": [vector_store.id]}
    ]
)

print(response.output_text)

Core flow: User → Responses API → Model → Tool → Result → Model → Response

For file_search, documents are stored in a vector store and prepared for semantic retrieval:

Files → Chunking → Embeddings → Vector Store → Retrieval → Model

This lets the model answer using relevant document content rather than relying only on its trained knowledge. Uploaded company policies or private documents → File Search + Vector Store.

Function Calling

Functions are different because the application executes the function, not the model. The model identifies the required function and returns a function-call request:

User → Model → Function Call → Application → Function → Result → Model → Response

The application executes the requested code and returns its result. This process can run in a loop when multiple tool calls are needed.

Key distinction: built-in tools extend the model with predefined capabilities; function calling connects the model to your own application logic and actions.

Related Snipps on Snippset

• What Microsoft Foundry Provides for Building AI Agents — Overview of Foundry capabilities for models, agents, tools, data, security, and monitoring.
• Microsoft Foundry SDK vs. Foundry Tools SDKs — Explains the difference between platform-level Foundry development and specialized AI service SDKs.
• Building Production-Ready AI Agents with Microsoft Agent Framework Course — Covers tools, memory, workflows, orchestration, monitoring, and production-ready AI agents.