M365 Agents SDK: Multi-Agent Patterns

Key insights

• Microsoft 365 Agents SDK: A hosting framework for building and running multiple AI agents across Microsoft 365 and Azure. 
  It lets teams create agents that share work, tools, and data to automate complex business tasks.
• Multi-agent patterns: Use coordinated agents instead of a single assistant to split large requests into smaller jobs. 
  Example: one agent pulls CRM data, another drafts a document, and a third schedules meetings in Outlook.
• Orchestrator-worker: A lead agent breaks the task into parts and delegates to specialist subagents. 
  This keeps each agent focused, reduces context overload, and improves reliability and scalability.
• Activity protocol: A common message format and channel adapters let agents communicate and connect across services and external orchestrators. 
  That standard makes it easier to integrate Copilot Studio, Azure AI, and custom agents.
• On-behalf-of (OBO) auth: Secure delegated access lets agents act for users without exposing credentials. 
  OBO tokens help protect data flow between agents and Microsoft services.
• VS Code Toolkit & samples: Microsoft provides a VS Code extension and sample projects to speed development and testing. 
  The SDK reached private preview and public previews rolled out in mid-2025, with tools for Copilot Studio and Azure AI integration.

Overview of the presentation

On June 17, 2025, Microsoft published a video titled "Multi-Agent patterns with the Microsoft 365 Agents SDK" that outlines a new approach to coordinating AI agents across Microsoft services. The session, delivered by a Microsoft presenter, introduces how teams can route tasks between tools such as Copilot Studio and Azure AI Foundry, while also highlighting practical features like the activity protocol and channel adapters. In addition, the video demonstrates secure delegation patterns including on‑behalf‑of or OBO authentication and previews the companion VS Code Toolkit for development workflows. Therefore, the content aims to help developers and IT leaders see how multi-agent systems can fit into existing business processes.

How the multi-agent model works

The video explains that the Microsoft 365 Agents SDK supports a modular hosting model where a lead agent decomposes complex requests and delegates subtasks to specialized subagents. Consequently, each subagent focuses on a specific domain or tool, which reduces context overload on any single component and helps with scaling. For example, one subagent might extract CRM data, another could generate a Word draft, and a third could schedule meetings, all coordinated by a central orchestrator. This orchestrator-worker pattern emphasizes clear role separation and predictable handoffs among agents.

Key features and integrations

Moreover, the presentation highlights integrations across Microsoft platforms, enabling agents to communicate using a common activity protocol and to plug into channel adapters for apps like Microsoft 365 and other services. The SDK is deliberately unopinionated about underlying models, so teams may mix agents built in Copilot Studio, Azure AI services, or custom engines, which increases flexibility. In addition, Microsoft demonstrated sample projects and tooling in the VS Code Toolkit, helping developers prototype and debug agent flows more quickly. Therefore, the platform seeks to balance openness with ready-made components that accelerate adoption.

Practical applications and benefits

The video frames multi-agent patterns as useful for cross-functional workflows such as executive briefings, customer onboarding, and incident management, where tasks span people, apps, and data. By enabling agents to specialize and coordinate, organizations can automate multi-step processes that previously required manual handoffs or brittle integrations. Furthermore, the approach supports connecting to external knowledge sources and orchestrators, which helps maintain continuity across complex processes. As a result, teams can choose targeted automation that reduces repetitive work while preserving business context.

Tradeoffs and implementation challenges

Despite the advantages, the video acknowledges several tradeoffs that organizations must consider when adopting a multi-agent design. For instance, distributing work among many agents increases architectural complexity and makes debugging harder, especially when agents use different models or vendors. In addition, there is a balance between security and convenience: mechanisms like OBO auth add necessary protection but also require careful identity and permission management, which can slow deployment. Consequently, teams need to weigh these costs against expected gains in scale, reliability, and domain specialization.

Operational concerns and governance

Operationally, the presentation points to governance, monitoring, and cost control as key concerns when running agent ecosystems in production. For example, mixed-model deployments can produce variable latency and unpredictable cost profiles, so planning observability and budgets up front helps avoid surprises. Moreover, ensuring consistent data handling across agents is essential for privacy and compliance, which means adopting clear policies and technical safeguards for data flow. Therefore, successful implementations will pair the SDK’s flexibility with strong governance practices.

Patterns and architectural choices

The video contrasts two main architectural choices: a centralized orchestrator that controls workflows versus more decentralized coordination where agents negotiate responsibilities. A central orchestrator simplifies tracing and policy enforcement, but it can become a single point of failure or a performance bottleneck. Conversely, decentralized designs can offer resilience and local autonomy, yet they demand more sophisticated protocols for consistency and conflict resolution. Thus, teams must evaluate their tolerance for complexity, latency, and governance overhead when selecting a pattern.

Getting started and next steps

The presenter recommends starting with small, well-scoped use cases to learn operational patterns and security needs before expanding to broad automation scenarios. In addition, Microsoft provides samples and tooling to accelerate prototyping, which reduces early friction and helps teams validate value quickly. Looking ahead, public previews of these multi-agent capabilities were expected to broaden access, allowing more organizations to experiment with agent orchestration at scale. Ultimately, the video invites developers and IT leaders to test patterns, measure outcomes, and refine governance to realize the promise of coordinated AI agents.

Keywords

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