Azure AI Foundry Agents: Building Production AI Agents

Azure AI Foundry provides a complete platform for building and deploying AI agents. Here’s how to use it.

AI Foundry Agent Development

from azure.ai.foundry import AIFoundryClient
from azure.ai.foundry.agents import Agent, Tool, Memory
from azure.identity import DefaultAzureCredential

# Initialize client
client = AIFoundryClient(
    endpoint="https://myworkspace.api.azureml.ms",
    credential=DefaultAzureCredential()
)

# Define tools
@Tool.function
async def search_documents(query: str, top_k: int = 5) -> list:
    """Search internal documents for relevant information."""
    results = await document_service.search(query, top_k)
    return [{"title": r.title, "content": r.content} for r in results]

@Tool.function
async def query_database(sql: str) -> dict:
    """Execute SQL query against the analytics database."""
    return await database_service.execute(sql)

@Tool.function
async def create_ticket(title: str, description: str, priority: str) -> dict:
    """Create a support ticket in the ticketing system."""
    return await ticket_service.create(title, description, priority)

# Create agent
agent = client.agents.create(
    name="CustomerServiceAgent",
    model="gpt-4o",
    instructions="""You are a customer service agent.
    Help customers with their inquiries by:
    1. Searching documentation for answers
    2. Looking up their account information
    3. Creating support tickets when needed
    Always be helpful and professional.""",
    tools=[search_documents, query_database, create_ticket],
    memory=Memory(
        type="conversation",
        window_size=20
    )
)

# Run agent
async def handle_customer_query(customer_id: str, query: str):
    thread = client.threads.create()

    # Add context
    thread.add_message(
        role="system",
        content=f"Customer ID: {customer_id}"
    )

    # Add customer query
    thread.add_message(
        role="user",
        content=query
    )

    # Run agent
    run = await client.runs.create(
        thread_id=thread.id,
        agent_id=agent.id
    )

    # Wait for completion
    result = await client.runs.wait(run.id)

    return result.messages[-1].content

Agent Deployment

# Deploy agent as endpoint
deployment = client.deployments.create(
    agent_id=agent.id,
    name="customer-service-prod",
    compute={
        "instance_type": "Standard_DS3_v2",
        "instance_count": 2
    },
    scaling={
        "min_replicas": 1,
        "max_replicas": 10,
        "target_utilization": 70
    }
)

# Get endpoint URL
endpoint_url = deployment.scoring_uri

Azure AI Foundry simplifies the path from agent prototype to production deployment.