From Concept to Code: Building Production-Ready Multi-Agent Systems with Microsoft Foundry

Microsoft

Nov 25, 2025

We have reached a critical inflection point in AI development. Within the Microsoft Foundry ecosystem, the core value proposition of "Agents" is shifting decisively—moving from passive content generation to active task execution and process automation.

These are no longer just conversational interfaces. They are intelligent entities capable of connecting models, data, and tools to actively execute complex business logic.

To support this evolution, Microsoft has introduced a powerful suite of capabilities: the Microsoft Agent Framework for sophisticated orchestration, the Agent V2 SDK, and integrated Microsoft Foundry VSCode Extensions. These innovations provide the tooling necessary to bridge the gap between theoretical research and secure, scalable enterprise landing.

But how do you turn these separate components into a cohesive business solution? That is the challenge we address today. This post dives into the practical application of these tools, demonstrating how to connect the dots and transform complex multi-agent concepts into deployed reality.

The Scenario: Recruitment through an "Agentic Lens"

Let’s ground this theoretical discussion with a real-world scenario that perfectly models a multi-agent environment: The Recruitment Process.

By examining recruitment through an agentic lens, we can identify distinct entities with specific mandates:

The Recruiter Agent: Tasked with setting boundary conditions (job requirements) and preparing data retrieval mechanisms (interview questions).
The Applicant Agent: Objective is to process incoming queries and synthesize the best possible output to meet the recruiter's acceptance criteria.

Phase 1: Design

Achieving Orchestration via Microsoft Foundry Workflows

To bridge the gap between our scenario and technical reality, we start with Foundry Workflows.

Workflows serves as the visual integration environment within Foundry. It allows you to build declarative pipelines that seamlessly combine deterministic business logic with the probabilistic nature of autonomous AI agents.

By adopting this visual, low-code paradigm, you eliminate the need to write complex orchestration logic from scratch. Workflows empowers you to coordinate specialized agents intuitively, creating adaptive systems that solve complex business problems collaboratively.

Visually Orchestrating the Cycle

Microsoft Foundry provides an intuitive, web-based drag-and-drop interface. This canvas allows you to integrate specialized AI agents alongside standard procedural logic blocks, transforming abstract ideas into executable processes without writing extensive glue code.

To translate our recruitment scenario into a functional workflow, we follow a structured approach:

Agent Prerequisites: We pre-configure our specialized agents within Foundry. We create a Recruiter Agent (prompted to generate evaluation criteria) and an Applicant Agent (prompted to synthesize responses).
Orchestrating the Interaction: We drag these nodes onto the board and define the data flow. The process begins with the Recruiter generating questions, piping that output directly as input for the Applicant agent.
Adding Business Logic: A true workflow requires decision-making. We introduce control flow logic, such as IF/ELSE conditional blocks, to evaluate the recruiter's questions based on predefined criteria. This allows the workflow to branch dynamically—if satisfied, the candidate answers the questions; if not, the questions are regenerated.

Alternative: YAML Configuration

For developers who prefer a code-first approach or wish to rapidly replicate this logic across environments, Foundry allows you to export the underlying YAML.

kind: workflow
trigger:
  kind: OnConversationStart
  id: trigger_wf
  actions:
    - kind: SetVariable
      id: action-1763742724000
      variable: Local.LatestMessage
      value: =UserMessage(System.LastMessageText)
    - kind: InvokeAzureAgent
      id: action-1763736666888
      agent:
        name: HiringManager
      input:
        messages: =System.LastMessage
      output:
        autoSend: true
        messages: Local.LatestMessage
    - kind: Question
      variable: Local.Input
      id: action-1763737142539
      entity: StringPrebuiltEntity
      skipQuestionMode: SkipOnFirstExecutionIfVariableHasValue
      prompt: Boss, can you confirm this ?
    - kind: ConditionGroup
      conditions:
        - condition: =Local.Input="Yes"
          actions:
            - kind: InvokeAzureAgent
              id: action-1763744279421
              agent:
                name: ApplyAgent
              input:
                messages: =Local.LatestMessage
              output:
                autoSend: true
                messages: Local.LatestMessage
            - kind: EndConversation
              id: action-1763740066007
          id: if-action-1763736954795-0
      id: action-1763736954795
      elseActions:
        - kind: GotoAction
          actionId: action-1763736666888
          id: action-1763737425562
id: ""
name: HRDemo
description: ""

Simulating the End-to-End Process

Once constructed, Foundry provides a robust, built-in testing environment. You can trigger the workflow with sample input data to simulate the end-to-end cycle. This allows you to debug hand-offs and interactions in real-time before writing a single line of application code.

Phase 2: Develop

From Cloud Canvas to Local Code with VSCode

Foundry Workflows excels at rapid prototyping. However, a visual UI is rarely sufficient for enterprise-grade production. The critical question becomes: How do we integrate these visual definitions into a rigorous Software Development Lifecycle (SDLC)?

While the cloud portal is ideal for design, enterprise application delivery happens in the local IDE. The Microsoft Foundry VSCode Extension bridges this gap.

This extension allows developers to:

Sync: Pull down workflow definitions from the cloud to your local machine.
Inspect: Review the underlying logic in your preferred environment.
Scaffold: Rapidly generate the underlying code structures needed to run the flow.

This accelerates the shift from "understanding" the flow to "implementing" it.

Phase 3: Deploy

Productionizing Intelligence with the Microsoft Agent Framework

Once the multi-agent orchestration has been validated locally, the final step is transforming it into a shipping application.

This is where the Microsoft Agent Framework shines as a runtime engine. It natively ingests the declarative Workflow definitions (YAML) exported from Foundry. This allows artifacts from the prototyping phase to be directly promoted to application deployment.

By simply referencing the workflow configuration libraries, you can "hydrate" the entire multi-agent system with minimal boilerplate. Here is the code required to initialize and run the workflow within your application.

Note - Check the source code https://github.com/microsoft/Agent-Framework-Samples/tree/main/09.Cases/MicrosoftFoundryWithAITKAndMAF

Summary: The Journey from Conversation to Action

Microsoft Foundry is more than just a toolbox; it is a comprehensive solution designed to bridge the chasm between theoretical AI research and secure, scalable enterprise applications.

In this post, we walked through the three critical stages of modern AI development:

Design (Low-Code): Leveraging Foundry Workflows to visually orchestrate specialized agents (Recruiter vs. Applicant) mixed with deterministic business rules.
Develop (Local SDLC): Utilizing the VSCode Extension to break down the barriers between the cloud canvas and the local IDE, enabling seamless synchronization and debugging.
Deploy (Native Runtime): Using the Microsoft Agent Framework to ingest declarative YAML, realizing the promise of "Configuration as Code" and eliminating tedious logic rewriting.

By following this path, developers can move beyond simple content generation and build adaptive, multi-agent systems that drive real business value.