lakshaymalik provides a thorough, practical guide to building multi-agent AI systems with Azure AI Foundry, detailing architectural patterns, workflow design, real-world scenarios, and best practices for enterprise-grade deployment.

Multi-Agent Orchestration with Azure AI Foundry: From Idea to Production

Enterprise workflows today are increasingly complex, involving multiple systems, real-time operations, and strict compliance requirements. Traditional, monolithic AI solutions are not flexible enough—multi-agent approaches offer more scalability and reliability.

Why Multi-Agent, and Why Now?

Business processes span data retrieval, policy validation, approvals, and often require human sign-off. Azure AI Foundry introduces:

  • Agent Service: Design goal-directed agents, add enterprise tool integrations, and apply policies.
  • Connected Agents (A2A): Agents as modular “tools” delegated by an orchestrator for specialized tasks—communication and routing becomes simple.
  • Multi-Agent Workflows: Adds state for context persistence, managed retries, compensation, and long-running steps—ideal for multi-stage approvals and exception handling.
  • Interoperability: Use MCP for context sharing and A2A for agent-to-agent messaging—works across Azure and external clouds.
  • Observability & Trust: In-built tracing, metrics, evaluation, and guardrails ensure quality, security, and transparency.

Reference Architecture

A typical flow:

  1. User/System trigger starts process.
  2. Orchestrator Agent receives the goal, delegates subtasks to:
    • Retrieval Agent (e.g., with Azure AI Search, Fabric/OneLake)
    • Analysis Agent (performs calculations or code execution)
    • Policy Agent (handles entitlements, responsible AI, approvals)
    • Action Agent (updates systems using OpenAPI, Logic Apps, Functions)
  3. Results are consolidated by Orchestrator, optionally reviewed by a human, then audited and logged using Foundry’s observability features.

Real-World Scenarios

1. Customer Support Autopilot

  • Flow: Orchestrator receives a support ticket, classifies it, Retrieval Agent fetches history, Analysis Agent drafts a fix, Policy Agent checks entitlements, Action Agent updates CRM and sends a response.
  • Benefit: Dividing tasks among distinct agents makes the workflow more resilient and troubleshooting faster.

2. Financial Approvals

  • Flow: Orchestrator parses invoices, Extraction Agent gathers data, Risk Agent screens for anomalies, Policy Agent ensures compliance and limits, actions gate through human approval, Action Agent posts results to ERP.
  • Benefit: Stateful, auditable workflows fit approval chains and compliance requirements.

3. Supply Chain Exception Handling

  • Flow: Orchestrator ingests events, Forecast Agent measures impact, Vendor Agent queries lead times, Plan Agent suggests mitigation, Policy Agent validates, Action Agent initiates changes.
  • Benefit: Specialization reduces latency, orchestrator remains streamlined.

Building Your First Connected-Agent Workflow

1. Create Project & Deploy Models

  • Provision distinct models for orchestrator and specialist agents.
  • Integrate data sources like Azure AI Search, Fabric/OneLake, SharePoint, and register system tools (OpenAPI, Logic Apps, Functions).

2. Define Agent Roles

  • Orchestrator: Coordinates and compiles results
  • Retrieval: Provides enterprise data grounding
  • Analysis: Runs calculations and code
  • Policy: Checks rules, entitlements
  • Action: Executes side-effectful system updates

3. Register Connected Agents (A2A)

  • In the portal, add each specialist as an orchestrator tool. In SDK/CLI, reference child agent IDs in orchestrator configuration.

Conceptual Example (Pseudocode):

orchestrator = agents.create(
    name="orchestrator",
    instructions="You coordinate specialists. Delegate, verify, and compile final answers."
)
retrieval = agents.create(name="retrieval", tools=["azure_ai_search:kb_index"])
analysis  = agents.create(name="analysis",  tools=["code_interpreter"])
policy    = agents.create(name="policy",    tools=["policy_rules:mcp"])
action    = agents.create(name="action",    tools=["openapi:erp","logicapp:notify"])

# Connect specialists as 'tools' on the orchestrator (A2A)

agents.connect(parent=orchestrator.id, children=[retrieval.id, analysis.id, policy.id, action.id])

4. Orchestrate the Workflow

  • Set stateful steps, retry/backoff policies, and routes for human-in-the-loop approvals where required. Example workflow pseudocode:
steps:
  - delegate: retrieval
    retry: {max: 2, backoff: expo}
  - parallel:
      - analysis
      - policy
  - gate:
      type: human_approval
      condition: "${policy.limit_exceeded == true}"
  - delegate: action
audit:
  trace: enabled
  pii_redaction: strict

5. Monitor, Evaluate, and Improve

  • Use tracing, metrics, and evaluation features
  • Compare prompts, tools, and model mixes
  • Assign strict cost/latency budgets and use content safety measures

Design Advice

  • Start with two agents—expand only if needed
  • Make responsibilities clear and isolated
  • Steps should be idempotent; leverage correlation IDs for tracking
  • Use human-approval gates for irreversible or sensitive actions
  • Monitor cost and latency closely
  • Route exceptions and approvals to humans, ensuring traceability

Helpful Resources

  • Azure AI Foundry: Multi-agent overview, A2A, MCP, workflows
  • How-To Guides: Building connected agents
  • AI agent orchestration patterns (Microsoft Architecture Center)
  • TechCommunity explainers and Learn modules

Article by lakshaymalik. For profile and more insights, see the original post on the Azure Infrastructure Blog.

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