Building Multi-Agent Systems with Azure AI Foundry Orchestration

Complex business processes require multiple specialized AI agents working together. Azure AI Foundry’s orchestration layer, announced at Build 2025, provides the infrastructure for building robust multi-agent systems. Here’s how to implement agent collaboration patterns.

Defining Agent Roles

Each agent specializes in a specific domain, with clear handoff protocols:

from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import Agent, AgentOrchestrator, HandoffRule

project = AIProjectClient(subscription_id, resource_group, project_name, credential)

# Define specialized agents
research_agent = Agent(
    name="research-agent",
    model="gpt-4o",
    instructions="""You research and gather information.
    When you have comprehensive findings, hand off to the analyst.""",
    tools=[{"type": "web_search"}, {"type": "knowledge_base", "id": "kb-docs"}]
)

analyst_agent = Agent(
    name="analyst-agent",
    model="claude-sonnet-4-20250514",
    instructions="""You analyze research findings and create structured reports.
    When analysis is complete, hand off to the writer.""",
    tools=[{"type": "code_interpreter"}]
)

writer_agent = Agent(
    name="writer-agent",
    model="gpt-4o",
    instructions="""You create polished, user-friendly content from analysis.
    You produce the final deliverable.""",
    tools=[]
)

# Create orchestrator with handoff rules
orchestrator = AgentOrchestrator(
    name="content-pipeline",
    agents=[research_agent, analyst_agent, writer_agent],
    entry_agent="research-agent",
    handoff_rules=[
        HandoffRule(
            from_agent="research-agent",
            to_agent="analyst-agent",
            condition="research_complete"
        ),
        HandoffRule(
            from_agent="analyst-agent",
            to_agent="writer-agent",
            condition="analysis_complete"
        )
    ],
    max_iterations=10,
    timeout_seconds=300
)

pipeline = project.orchestrators.create(orchestrator)

Running the Pipeline

Execute the multi-agent workflow with a single request:

async def process_request(topic: str):
    result = await project.orchestrators.run(
        orchestrator_id=pipeline.id,
        input_message=f"Create a comprehensive report on: {topic}",
        stream=True
    )

    async for event in result:
        print(f"[{event.agent}] {event.message}")

    return result.final_output

Monitoring and Debugging

Azure AI Foundry provides trace visualization showing the complete agent interaction graph, making it easy to debug complex workflows and optimize handoff conditions.