Microsoft Releases ‘Microsoft Agent Framework’: An Open-Source SDK and Runtime that Simplifies the Orchestration of Multi-Agent Systems

Microsoft Releases ‘Microsoft Agent Framework’: An Open-Source SDK and Runtime that Simplifies the Orchestration of Multi-Agent Systems

Estimated reading time: 6 minutes

• The Microsoft Agent Framework unifies AutoGen and Semantic Kernel into a single, open-source SDK and runtime for building production-grade multi-agent AI systems.
• It offers flexible orchestration with both LLM-driven Agent Orchestration for dynamic planning and deterministic Workflow Orchestration for precise, rule-based tasks.
• Designed for enterprise scale, the framework provides robust production controls, integrated telemetry, and seamless deployment with Azure AI Foundry’s Agent Service.
• Developers benefit from multi-language SDKs (Python, .NET), extensive model/provider flexibility (Azure OpenAI, OpenAI, Ollama), and reduced “glue code” for complex AI applications.
• The framework supports critical features like thread-based state management for context preservation, OpenTelemetry hooks for debugging, and strong security protocols via Azure AI Foundry.

• Unifying AI Powerhouses: AutoGen Meets Semantic Kernel for Enterprise-Grade Solutions
• Orchestration Flexibility: From Creative Planning to Deterministic Workflows
• Production-Ready at Scale: Security and Observability with Azure AI Foundry
• Developer-Centric Architecture: Notes on Flexibility and Design
  • Real-World Impact: Revolutionizing Customer Service
  • 3 Actionable Steps to Get Started
• Conclusion
• Ready to Orchestrate Your AI Future?
• Frequently Asked Questions (FAQ)

The evolving landscape of artificial intelligence is increasingly characterized by the complexity of multi-agent systems. These systems, where multiple AI entities collaborate to achieve a common goal, hold immense promise but also introduce significant challenges in development, deployment, and management. From managing diverse AI models to ensuring seamless communication and robust production controls, the “glue code” required to stitch these components together often becomes a costly and fragile bottleneck for enterprises.

Addressing this critical need for streamlined development and reliable operations, Microsoft has announced a pivotal advancement: the Microsoft Agent Framework. This open-source SDK and runtime is meticulously engineered to transform how developers approach multi-agent AI, consolidating previously disparate functionalities into a unified, enterprise-ready platform. It aims to simplify orchestration, enhance observability, and provide a clear, efficient path to production for advanced AI applications.

Unifying AI Powerhouses: AutoGen Meets Semantic Kernel for Enterprise-Grade Solutions

At the core of this strategic release is a significant consolidation of Microsoft’s leading AI agent initiatives. Microsoft released the Microsoft Agent Framework (public preview), an open-source SDK and runtime that unifies core ideas from AutoGen (agent runtime and multi-agent patterns) with Semantic Kernel (enterprise controls, state, plugins) to help teams build, deploy, and observe production-grade AI agents and multi-agent workflows. The framework is available for Python and .NET and integrates directly with Azure AI Foundry’s Agent Service for scaling and operations.

What exactly is Microsoft delivering with this framework? Essentially, it’s a consolidated agent runtime and API surface. The Agent Framework smartly carries forward AutoGen’s proven single- and multi-agent abstractions, which have been widely adopted for their effectiveness in collaborative AI scenarios. To this foundation, it integrates Semantic Kernel’s powerful enterprise features, including thread-based state management—a crucial element for maintaining context and ensuring reproducibility across intricate interactions. Developers will benefit from enhanced type safety, comprehensive filtering capabilities, built-in telemetry for performance monitoring, and broad support for various models and embedding technologies.

Microsoft carefully positions this framework not as a replacement that negates the value of AutoGen or Semantic Kernel, but rather as their successor, collaboratively built by the same expert teams. This approach ensures continuity, leverages the best of both worlds, and offers developers a more powerful, streamlined toolkit designed for the demands of modern AI.

Orchestration Flexibility: From Creative Planning to Deterministic Workflows

A key distinguishing feature of the Microsoft Agent Framework is its sophisticated approach to orchestration. It introduces first-class support for two distinct, yet complementary, orchestration modes, acknowledging the diverse requirements of contemporary AI applications:

• Agent Orchestration: This mode is driven by Large Language Models (LLMs), enabling dynamic, LLM-driven decision-making. Agents can creatively plan, adapt to new information, and engage in complex reasoning, mirroring human-like collaboration and problem-solving.
• Workflow Orchestration: For scenarios demanding precision and predictability, this mode facilitates deterministic, business-logic-driven multi-agent flows. It ensures reliable handoffs, enforces predefined constraints, and is perfectly suited for structured business processes and automated operations where accuracy is paramount.

This dual capability empowers the creation of innovative hybrid systems where the creative planning of LLMs can coexist seamlessly with reliable, rule-based processes. For instance, an AI system could brainstorm marketing strategies (using agent orchestration) and then precisely execute a multi-step campaign launch (via workflow orchestration) with integrated checks and balances.

Furthermore, the framework emphasizes pro-code and platform interoperability. Its base AIAgent interface is engineered for maximum flexibility, allowing developers to effortlessly swap chat model providers. Critically, it interoperates directly with Azure AI Foundry Agents, OpenAI Assistants, and even Copilot Studio. This thoughtful design significantly mitigates vendor lock-in at the application layer, granting enterprises the freedom to select the best tools for their specific needs and evolve their AI infrastructure without prohibitive re-writes.

In alignment with its open-source philosophy, the Microsoft Agent Framework provides multi-language SDKs under an MIT license. The GitHub repository already hosts comprehensive Python and .NET packages, complete with illustrative examples and CI/CD-friendly scaffolding. While AutoGen itself will continue to receive maintenance for bug fixes and security patches, Microsoft’s clear guidance recommends considering the Agent Framework for all new multi-agent system builds, signaling its position as the future for these complex projects.

Production-Ready at Scale: Security and Observability with Azure AI Foundry

The journey from an AI agent prototype to a reliably managed production system is often fraught with challenges. Microsoft’s integrated vision addresses this directly by designating Azure AI Foundry’s Agent Service as the production home for the Agent Framework, offering a fully managed runtime environment.

This service is purpose-built to tackle the complexities of enterprise-grade AI head-on. It expertly links various models, tools, and frameworks; robustly manages thread state across interactions; rigorously enforces content safety and identity protocols; and seamlessly integrates comprehensive observability capabilities. Crucially, it inherently supports multi-agent orchestration and distinguishes itself from Copilot Studio’s more low-code approach by specifically targeting complex, pro-code enterprise scenarios that demand granular control, customizability, and scalability.

The implications for “AI economics” are profound. Enterprise AI economics are heavily influenced by factors such as token throughput, latency, efficient failure recovery mechanisms, and insightful observability. Microsoft’s consolidation strategy directly confronts these cost drivers by:

1. Providing a single, unified runtime abstraction for efficient agent collaboration and tool use.
2. Attaching critical production controls—including telemetry, filters, identity/networking, and safety features—to this same abstraction, significantly reducing the need for custom, error-prone implementations.
3. Leveraging deployment onto a managed service that inherently handles scaling, policy enforcement, and diagnostics, offloading substantial operational burden from development teams.

This holistic approach dramatically reduces the “glue code” that typically drives considerable cost, fragility, and brittleness in bespoke multi-agent systems. It aligns perfectly with Azure AI Foundry’s established model-catalog and toolchain approach, offering a cohesive and powerful ecosystem for advanced AI development.

Developer-Centric Architecture: Notes on Flexibility and Design

The architectural principles underpinning the Microsoft Agent Framework prioritize clarity, control, and flexibility for developers:

• Runtime & State Management: Agents coordinate within a sophisticated runtime that manages their lifecycles, identities, communication protocols, and security boundaries. These foundational concepts are inherited and further formalized from AutoGen, ensuring a robust and familiar operational environment. Threads serve as the fundamental unit of state, a critical feature that enables reproducible runs, efficient retries, and comprehensive audit trails—all essential for debugging, compliance, and maintaining context in complex, long-running systems.
• Functions & Plugins: The framework smartly leverages Semantic Kernel’s well-established plugin architecture and powerful function-calling capabilities. This integration allows developers to seamlessly bind diverse tools—from code interpreters and knowledge retrieval systems to custom-built business functions—into agent policies using clearly typed contracts, promoting reliability, reusability, and maintainability.
• Model/Provider Flexibility: One of the most compelling aspects for developers is the framework’s expansive interoperability. The same underlying agent interface can target a wide array of model providers, including Azure OpenAI, OpenAI, local runtimes (such as Ollama or Foundry Local), and GitHub Models. This unparalleled flexibility empowers teams to fine-tune their cost-performance ratio for each specific task without the need for extensive rewriting of orchestration logic, making it a truly adaptable solution for diverse deployment strategies and evolving AI landscapes.

Real-World Impact: Revolutionizing Customer Service

Imagine a large e-commerce company grappling with escalating customer service costs and inconsistent response times. Implementing a multi-agent system powered by the Microsoft Agent Framework could fundamentally transform their operations. An initial “Triage Agent,” leveraging an LLM, could receive inbound customer queries, classify their intent (e.g., order tracking, technical support, billing issue), and intelligently route them. A “Logistics Agent” could then automatically check order status using internal APIs and provide an immediate response. If the issue is technical, a “Tech Support Agent” could engage, equipped with tools to access documentation and troubleshoot common problems. For billing disputes, a “Billing Agent” could access payment systems and initiate refunds or adjustments. The framework’s thread-based state management ensures context is seamlessly maintained across agents, preventing repetitive questioning. Its integrated observability features allow the company to monitor agent performance, identify bottlenecks, and continuously improve the system, leading to significantly faster resolution times and enhanced customer satisfaction.

3 Actionable Steps to Get Started

1. Explore the SDKs: Dive into the open-source GitHub repository for the Microsoft Agent Framework. Experiment with the Python and .NET packages, thoroughly examining the provided examples to understand basic agent creation, communication patterns, and tool integration.
2. Experiment with Orchestration Modes: Begin building simple multi-agent systems to grasp the framework’s capabilities. Try scenarios using LLM-driven agent orchestration for creative problem-solving and then build a deterministic workflow orchestration for a structured, rule-based task to understand the unique strengths and ideal use cases for each mode.
3. Consider Azure AI Foundry for Production: As your prototypes mature and demonstrate value, investigate Azure AI Foundry’s Agent Service. Understand how its managed runtime, built-in security features, identity management, and comprehensive observability can streamline the deployment and robust operation of your production-grade multi-agent applications.

Conclusion

The Microsoft Agent Framework represents a strategic and timely evolution in the realm of AI development. By effectively collapsing two previously divergent, yet powerful, stacks—AutoGen’s dynamic multi-agent runtime and Semantic Kernel’s robust enterprise plumbing—into a single, coherent API surface, Microsoft provides a clear, managed path to production for even the most ambitious multi-agent systems. The framework’s profound emphasis on thread-based state management, OpenTelemetry hooks for detailed tracing, and extensive model/provider flexibility directly addresses common pain points such as reproducibility, latency debugging, and efficient failure triage that often plague complex agentic systems.

With Azure AI Foundry’s Agent Service assuming critical responsibilities like identity management, content safety enforcement, and sophisticated tool orchestration, development teams are empowered to shift their focus from wrestling with intricate infrastructure to crafting truly intelligent and impactful agent policies. This release firmly underscores Microsoft’s unwavering commitment to fostering an interoperable, standard-friendly “agentic” future, equipping developers with the cutting-edge tools necessary to build sophisticated, scalable, and economically viable AI solutions for the enterprise.

Ready to Orchestrate Your AI Future?

Embark on your journey with the Microsoft Agent Framework today. Visit the official GitHub repository to access the latest SDKs, insightful examples, and comprehensive documentation. Explore how Azure AI Foundry can bring your visionary multi-agent systems to life in a secure, scalable, and production-ready environment. The future of collaborative AI is here, and Microsoft is providing the essential tools to build it.

Frequently Asked Questions (FAQ)

• What is the Microsoft Agent Framework?

  The Microsoft Agent Framework is an open-source SDK and runtime developed by Microsoft that unifies core concepts from AutoGen and Semantic Kernel. Its purpose is to simplify the building, deployment, and observation of production-grade multi-agent AI systems and workflows.

• How does the Microsoft Agent Framework combine AutoGen and Semantic Kernel?

  The framework integrates AutoGen’s robust agent runtime and multi-agent patterns with Semantic Kernel’s enterprise controls, state management (especially thread-based), and plugin architecture. This consolidation provides a unified API surface, enhanced type safety, telemetry, and broader model support, leveraging the strengths of both predecessors.

• What are the two main orchestration modes supported by the framework?

  The Microsoft Agent Framework supports two distinct orchestration modes: Agent Orchestration, which is LLM-driven for dynamic, creative decision-making, and Workflow Orchestration, which is deterministic and business-logic-driven for precise, predictable multi-agent flows and automated operations.

• How does the framework support production-grade AI systems and integrate with Azure AI Foundry?

  It designates Azure AI Foundry’s Agent Service as its production home, offering a fully managed runtime environment. This service provides enterprise-grade capabilities like robust thread state management, content safety, identity protocols, comprehensive observability, and scalability, offloading operational burden from development teams.

• Is the Microsoft Agent Framework open-source and what languages does it support?

  Yes, the Microsoft Agent Framework is open-source, released under an MIT license. It provides multi-language SDKs for both Python and .NET, complete with examples and CI/CD-friendly scaffolding in its GitHub repository.