Building AI Agents with Azure AI Foundry: A Post-Build 2025 Implementation Guide

Following Microsoft Build 2025’s announcements, Azure AI Foundry has emerged as the premier platform for building enterprise AI agents. This guide walks through implementing production-ready agents using the new SDK features released at Build.

Setting Up Your AI Foundry Project

The new Azure AI Foundry SDK simplifies agent creation with a unified programming model. Here’s how to bootstrap your first agent project:

from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import Agent, AgentCapability
from azure.identity import DefaultAzureCredential

# Initialize the project client
credential = DefaultAzureCredential()
project_client = AIProjectClient(
    subscription_id="your-subscription-id",
    resource_group="your-rg",
    project_name="customer-service-agents",
    credential=credential
)

# Define agent with specific capabilities
agent_config = Agent(
    name="support-assistant",
    model="gpt-4o",
    instructions="""You are a helpful customer support agent.
    Use the knowledge base to answer questions accurately.
    Escalate complex issues to human agents.""",
    capabilities=[
        AgentCapability.CODE_INTERPRETER,
        AgentCapability.FILE_SEARCH,
        AgentCapability.FUNCTION_CALLING
    ],
    tools=[
        {"type": "knowledge_base", "knowledge_base_id": "kb-support-docs"},
        {"type": "function", "function": escalate_to_human}
    ]
)

# Deploy the agent
agent = project_client.agents.create(agent_config)
print(f"Agent deployed: {agent.id}")

Key Build 2025 Enhancements

The new agent framework introduces several critical improvements. First, native multi-agent orchestration allows agents to collaborate without custom routing logic. Second, the integrated evaluation pipeline lets you test agent quality before deployment. Third, built-in content safety filters apply automatically based on your Azure AI Content Safety configuration.

Production Considerations

When deploying agents to production, configure proper authentication scopes, set up monitoring through Azure Monitor, and implement retry logic for transient failures. The new SDK handles connection pooling and rate limiting automatically, but you should still implement application-level circuit breakers for critical paths.

Azure AI Foundry represents a significant step forward in enterprise AI development, making it easier than ever to build reliable, scalable AI agents.