Multi-Agent Orchestration Patterns for Enterprise AI

As AI agents become more capable, the challenge shifts from building individual agents to orchestrating multiple agents working together. Today, let’s explore proven patterns for multi-agent orchestration.

Why Multi-Agent?

Single agents have limitations:

• Context window constraints
• Specialized knowledge requirements
• Reliability through redundancy
• Parallel task execution

Multi-agent systems address these by distributing work across specialized agents.

Pattern 1: Hub and Spoke (Coordinator Pattern)

A central coordinator agent delegates to specialist agents:

from azure.ai.foundry.agents import Agent, Coordinator

# Specialist agents
sql_agent = Agent(
    name="SQLExpert",
    instructions="You write and optimize SQL queries for data analysis.",
    tools=[execute_sql_tool, explain_plan_tool]
)

python_agent = Agent(
    name="PythonExpert",
    instructions="You write Python code for data transformation and ML.",
    tools=[execute_python_tool, install_package_tool]
)

viz_agent = Agent(
    name="VizExpert",
    instructions="You create data visualizations and dashboards.",
    tools=[create_chart_tool, create_dashboard_tool]
)

# Coordinator routes tasks
coordinator = Coordinator(
    name="DataTeamLead",
    agents=[sql_agent, python_agent, viz_agent],
    routing_strategy="semantic",  # Routes based on task semantics
    instructions="""You lead a data team. Analyze requests and delegate to:
    - SQLExpert for database queries
    - PythonExpert for code and ML tasks
    - VizExpert for visualizations
    Synthesize results into coherent responses."""
)

# Single entry point
result = await coordinator.run(
    task="Analyze customer churn: query the data, build a prediction model, and create a dashboard"
)

Pattern 2: Pipeline (Sequential Handoff)

Agents process work in sequence, each adding value:

from azure.ai.foundry.agents import Pipeline

# Define pipeline stages
pipeline = Pipeline(
    name="DataProcessingPipeline",
    stages=[
        Agent(
            name="Extractor",
            instructions="Extract and parse raw data from sources",
            output_schema={"raw_data": "DataFrame"}
        ),
        Agent(
            name="Transformer",
            instructions="Clean, transform, and enrich the data",
            input_requires=["raw_data"],
            output_schema={"clean_data": "DataFrame"}
        ),
        Agent(
            name="Analyzer",
            instructions="Perform statistical analysis and generate insights",
            input_requires=["clean_data"],
            output_schema={"insights": "Report"}
        ),
        Agent(
            name="Presenter",
            instructions="Format insights into executive summary",
            input_requires=["insights"],
            output_schema={"summary": "Document"}
        )
    ]
)

# Data flows through the pipeline
result = await pipeline.run(
    input={"source": "sales_database", "date_range": "2024-Q4"}
)

Pattern 3: Consensus (Voting Pattern)

Multiple agents independently solve a problem, then vote on the best solution:

from azure.ai.foundry.agents import ConsensusGroup

# Create redundant agents with different approaches
consensus_group = ConsensusGroup(
    name="ArchitectureReview",
    agents=[
        Agent(name="ConservativeArchitect",
              instructions="Prefer proven, stable solutions"),
        Agent(name="InnovativeArchitect",
              instructions="Consider cutting-edge approaches"),
        Agent(name="CostArchitect",
              instructions="Optimize for cost efficiency")
    ],
    consensus_threshold=0.66,  # 2/3 must agree
    resolution_strategy="synthesis"  # Combine best elements
)

result = await consensus_group.decide(
    question="What's the best architecture for our real-time analytics platform?",
    context=requirements_doc
)

print(f"Consensus: {result.decision}")
print(f"Agreement: {result.agreement_score}")
print(f"Dissenting views: {result.dissent}")

Pattern 4: Hierarchical (Management Chain)

Agents organized in management hierarchy for complex organizations:

from azure.ai.foundry.agents import Hierarchy

hierarchy = Hierarchy(
    name="DataOrganization",
    structure={
        "CTO": {
            "DataEngineering": {
                "ETLTeam": ["ETLDev1", "ETLDev2"],
                "PlatformTeam": ["PlatformEng1"]
            },
            "DataScience": {
                "MLTeam": ["MLEng1", "MLEng2"],
                "AnalyticsTeam": ["Analyst1"]
            }
        }
    },
    escalation_rules={
        "budget_approval": "manager",
        "architecture_decision": "CTO",
        "implementation": "self"
    }
)

# Request flows through hierarchy
result = await hierarchy.request(
    from_agent="ETLDev1",
    request="Need to upgrade Spark cluster - requires $5000/month",
    type="budget_approval"
)
# Automatically escalates to DataEngineering manager, then CTO if needed

Pattern 5: Blackboard (Shared State)

Agents collaborate through a shared knowledge board:

from azure.ai.foundry.agents import Blackboard, Observer

# Shared state all agents can read/write
blackboard = Blackboard(
    name="IncidentResponse",
    sections={
        "symptoms": [],
        "hypotheses": [],
        "evidence": [],
        "actions_taken": [],
        "resolution": None
    }
)

# Agents observe and contribute
detector = Observer(
    name="AnomalyDetector",
    watches=["metrics_stream"],
    writes_to="symptoms",
    trigger="anomaly_detected"
)

investigator = Observer(
    name="RootCauseInvestigator",
    watches=["symptoms"],
    writes_to="hypotheses",
    trigger="new_symptom"
)

resolver = Observer(
    name="AutoResolver",
    watches=["hypotheses", "evidence"],
    writes_to=["actions_taken", "resolution"],
    trigger="hypothesis_confirmed"
)

# System runs reactively
incident_system = blackboard.create_system([detector, investigator, resolver])
await incident_system.run_continuous()

Pattern 6: Swarm (Emergent Behavior)

Many simple agents producing complex collective behavior:

from azure.ai.foundry.agents import Swarm

# Simple agents with basic rules
swarm = Swarm(
    name="DataQualitySwarm",
    agent_template=Agent(
        instructions="Check one data quality rule. Report issues found.",
        tools=[check_rule_tool, report_issue_tool]
    ),
    count=50,  # 50 identical agents
    coordination="stigmergy",  # Indirect coordination through environment
    task_distribution="work_stealing"
)

# Swarm checks all DQ rules in parallel
results = await swarm.execute(
    task="Validate all data quality rules for customer_360 table",
    rules=dq_rule_catalog
)

# Aggregate findings
issues = swarm.aggregate(results, strategy="union")

Choosing the Right Pattern

Pattern	Best For	Complexity	Latency
Hub & Spoke	General task routing	Medium	Medium
Pipeline	Sequential processing	Low	High
Consensus	Critical decisions	Medium	High
Hierarchical	Enterprise processes	High	Variable
Blackboard	Collaborative investigation	High	Low
Swarm	Parallel simple tasks	Medium	Low

Implementation Considerations

Error Handling

from azure.ai.foundry.agents import CircuitBreaker, RetryPolicy

coordinator = Coordinator(
    agents=[agent1, agent2, agent3],
    error_handling={
        "retry_policy": RetryPolicy(max_attempts=3, backoff="exponential"),
        "circuit_breaker": CircuitBreaker(failure_threshold=5, reset_timeout=60),
        "fallback_agent": fallback_agent
    }
)

Observability

from azure.ai.foundry.agents import AgentTracer

tracer = AgentTracer(
    backend="azure_monitor",
    trace_level="detailed"
)

# All agent interactions are traced
with tracer.span("multi_agent_task"):
    result = await coordinator.run(task)

# View in Azure Monitor: agent calls, latencies, token usage, decisions

Multi-agent systems are the future of enterprise AI. Choose patterns that match your problem structure, and build in observability from day one.