An Overview of Microsoft Agent Framework and Its Value for Business

In April 2026, Microsoft released a production-ready Version 1.0 of its Microsoft Agent Framework, a solution that brings several AI agent development frameworks together and reflects the company’s broader AI services consolidation trend. Each release since has pushed it further, from a toolkit for building agents into a full platform for running and governing them.

This framework is Microsoft’s response to the growing demand for agentic AI, a market projected to grow at a compound annual rate of 42.14% in 2026-2031. This push is largely driven by hard numbers. For instance, in logistics alone, agentic AI solutions can cut operational costs by more than 20%.

But logistics is just one slice of what agentic AI, and Microsoft’s framework for building it, can do. In this article, we’ll break down what Microsoft Agent Framework actually is, how it works, and where it delivers real business value.

Microsoft Agent Framework is an open-source SDK and runtime for building, orchestrating, and deploying AI agents, available for both .NET and Python. One of its key properties is that the framework treats AI models as orchestrators that call existing functions and APIs, tools, and memory, making it a strong choice for building multi-agent AI systems.

Microsoft Agent Framework has two main pieces: agents, which combine a model with tools and memory, and workflows, which handle the business logic and coordinate multiple agents.

With Microsoft Agent Framework, AI development teams can:

• Build a single AI agent or complex multi-agent systems guided by a shared goal
• Connect AI agents with existing business tools, APIs, and data sources
• Give agents memory and long-running state, so they can handle tasks that span multiple steps or sessions
• Work across a wide range of AI providers, including Azure OpenAI, Anthropic Claude, Amazon Bedrock, and Google Gemini

An important thing about the framework is that it has human oversight built-in at the workflow level. An agent can pause mid-task, submit a specific action, such as a refund, a database write, or a message sent externally, for review and resume automatically once a human approves or rejects it. This principle is designed to minimize the risks of AI decisions and preserve human control over critical processes.

Microsoft Agent Framework is a direct successor to Semantic Kernel and AutoGen, two pioneering frameworks for AI agent development and multi-agent orchestration. Microsoft has since moved both predecessors into maintenance mode, focusing on the further evolution of the unified framework.

Microsoft Agent Framework absorbs both of its predecessors. The table below illustrates exactly what each one brought to the table and what changed after the merger.

Overall, Semantic Kernel and AutoGen solved different aspects of AI agent development — one gave developers stability and enterprise plumbing, the other gave them flexible multi-agent orchestration. Microsoft Agent Framework consolidates the best from both approaches in a single SDK.

Beyond unifying Semantic Kernel and AutoGen, Microsoft Agent Framework brings together a set of capabilities that make it viable for real enterprise deployments. Below are the ones that stand out.

Use of the Model Context Protocol (MCP)

Microsoft Agent Framework builds most agent and tool connections with the Model Context Protocol, an open standard for connecting AI applications to external tools and data sources.

In practice, this means an agent doesn’t need a custom-built connector for every external system it touches. The framework implements MCP support through two patterns: local MCP tools that execute directly in the client application, and hosted MCP tools that are managed and executed by AI services such as Azure AI. Whether it’s a GitHub repository, a filesystem, a database, or a proprietary internal tool, agents can discover and call it through the same standardized interface.

Agent to agent (A2A) protocol connection

Microsoft Agent Framework connects AI agents through the agent to agent protocol. It works as a structured messaging layer that allows agents to communicate across different environments.

For example, a claims-processing agent built on Semantic Kernel in a .NET environment can hand off a task to a fraud-detection agent running on LangGraph in Python. In this scenario, each side exposes an “agent card” describing its capabilities, while A2A handles the task negotiation and result exchange without either agent needing to know the other’s internal architecture.

This is especially valuable for organizations that operate with mixed technology stacks used for developing AI agents. With A2A, these agents are no longer siloed and can be composed into larger systems without a rewrite.

Solid control over multi-agent orchestration

Where Agent Framework goes further than either of its predecessors is in giving developers explicit control over how multiple agents work together. Agents are paired with a workflow, which is designed as an abstraction for expressing business or application logic as computation graphs. These agentic workflows define exactly how tasks move between specialized agents.

Microsoft Agent Framework’s 2026 build has also brought the Agent Harness and stable multi-agent orchestration patterns to general release. The harness gives a language model the infrastructure it needs to function as an agent, including tool use, memory, planning, and safety controls. As a result, teams don’t have to build that runtime themselves. The framework also provides a single interface for orchestration patterns, helping teams to define how agents coordinate without rewriting the underlying logic.

These features ensure faster deployment, lower engineering overhead, and greater flexibility. In fact, AI development teams can start with a simple infrastructure that coordinates two agents and evolve toward more complex orchestration models.

Enterprise-grade observability

Debugging agentic AI systems is typically very challenging because reasoning steps and tool calls happen behind the scenes, which makes some failures non-evident. Microsoft Agent Framework responds to this problem by introducing observability that emits traces, logs, and metrics according to OpenTelemetry GenAI semantic conventions, which are generally applied standards for tracking GenAI application behavior.

In practice, every agent run, tool call, and group chat turn is captured as an OpenTelemetry span. Thanks to that, the teams get full audit trails, latency breakdowns, and quality metrics in the same observability workspace as the rest of their application.

Combined with security features like Microsoft Entra ID authentication and Azure AI Content Safety, this gives compliance and security teams the audit trail they need to run agents in regulated environments.

Consistent path to production

One of Agent Framework’s biggest practical advantages is that the code your software engineers write locally is the same code that runs in production. Typically, this process looks as follows:

1. Developers build and test agents, tools, MCP integrations, and multi-step agentic workflows locally in .NET or Python.
2. A single Azure Developer CLI command provisions the required Azure resources, including Foundry, model deployment, Application Insights, and Container Registry.
3. The agent is packaged into a container, pushed to Azure Container Registry, and deployed to Foundry Agent Service with autoscaling and managed identity.
4. Each deployed agent gets its own Microsoft Entra identity, while every deployment is stored as an immutable versioned snapshot.
5. The same OpenTelemetry instrumentation works across local and production environments, giving teams a consistent view of agent behavior.

This parity means shorter timelines because it eliminates the need for a separate integration phase. Additionally, per-agent identities, versioned deployments, and shared observability carry the same governance and debugging clarity from prototype through production. As a result, teams can move fast without losing control as the number of deployed agents grows.

There are certain cases where the capabilities of Microsoft Agent Framework bring the most value.

Below, we explore the optimal use cases for the framework in more detail.

Research and retrieval agents

The framework’s support for MCP makes it a strong choice for building agents that retrieve information from large, fragmented data sources. Teams can expose each data source through the same standardized interface, and the agent calls them interchangeably.

This is especially relevant to organizations with distributed data architectures, where information is stored in multiple disconnected systems built at different times by different teams. With MCP, new data sources for AI agents can be plugged into the same interface without growing maintenance overhead sufficiently.

Coding agents

With native GitHub Copilot SDK integration, Microsoft Agent Framework can handle orchestration, delegate coding tasks to a Copilot SDK-powered agent, and pass the results downstream to the next agent in the pipeline. Rather than reimplementing coding capabilities, the framework coordinates specialized coding agents that already possess them.

The approach fits naturally into CI/CD workflows, where code review, testing, and deployment are each assigned to a dedicated agent. Since the framework’s role is limited to managing handoffs and state between these stages, teams gain an orchestration layer without sacrificing the coding-specific tooling they already rely on.

Compliance agents

Built-in security and observability is one of Microsoft Agent Framework’s key strengths. This feature is especially relevant to regulated environments in industries like healthcare or fintech, where every agent action needs to be traceable back to a cause.

This observability operates within the same runtime that governs the agent’s core loop, so the audit trail is generated automatically as part of the agent’s operation.

That reliability makes Microsoft Agent Framework an effective foundation for building compliance agents that monitor transactions, flag policy violations, or support audit workflows. Compliance teams get a verifiable record of what an agent did and why — the kind of evidence they need before they’ll approve automation in a regulated process.

Customer support assistants

Support agents that retrieve product or account information, recommend solutions, and escalate to a human when a request falls outside their scope. The framework’s human-in-the-loop approval steps that are built directly into the framework’s agentic workflow patterns make this practical. Sensitive actions, such as refunds and account changes, can require sign-off before execution, so a business gets the speed of automation on routine requests without handing an agent unsupervised authority over consequential ones.

Escalation and approval are first-class patterns in Microsoft Agent Framework. The solution allows engineers to tune exactly where the line sits between the tasks that can be handled by the agent independently and requests that require human oversight.

Microsoft 365 Copilot integration

Agents built with Agent Framework can extend into Microsoft 365 apps like Teams, Outlook, and Word, responding to mentions, notifications, and comments the way a human collaborator would. This works because the framework is built for the Microsoft ecosystem, sharing the same identity, deployment, and hosting model as the rest of the Foundry platform.

Combined with Agent 365’s governed access to mailboxes, calendars, and SharePoint, this allows organizations to integrate custom agents into the Microsoft 365 tools employees already use. There is no need to introduce a separate interface or workflow. For organizations standardized on Microsoft 365, this also supports adoption within existing user practices and IT governance.

Microsoft Agent Framework provides strong capabilities for building, deploying, and orchestrating AI agents and multi-agent systems. But you will need solid technical expertise to leverage it. Teams must design graph-based workflows, manage agent state and session context, configure model connections and middleware, and reason through multi-agent coordination logic. That takes developers fluent in .NET and the Microsoft stack, with genuine depth in AI software development and understanding of how to build an AI agent.

Leobit brings both. We have over 6 years of expertise in building AI solutions for both internal needs and client projects. Our specialists also have deep expertise across the Microsoft stack, while .NET development lies at the core of our services.

Leobit is a Microsoft Solutions Partner for Digital & App Innovation and Data & AI, which testifies to our technical expertise, certified talent, and a track record of delivering high-quality solutions on Microsoft technologies.

Industry recognition backs this up: Clutch named Leobit a Top AI Code Generation Company, and the team keeps sharpening its edge through advanced certifications, including Anthropic’s certification process, with Claude Certified Architects already on board.

Leobit is ready to help you put Microsoft Agent Framework to work, building agentic AI solutions that boost workflows and drive real business value.

Microsoft Agent Framework gives enterprises a single, production-ready path for building and running AI agents. It unifies Semantic Kernel’s stability and AutoGen’s flexibility into one SDK backed by long-term support. Built-in observability, human-in-the-loop controls, and enterprise identity management mean organizations can deploy agents in regulated, high-stakes environments without bolting on governance after the fact.

The framework sets a strong foundation for building AI agents for multiple purposes, including information retrieval, coding, compliance, customer support, as well as solutions that require integration with Microsoft 365.

The framework offers a variety of business-centric capabilities and has diverse use cases. But these use cases share one requirement: technical depth. Leobit brings that expertise, whether the goal is a complex agentic AI system or a single-purpose agent deployment. Contact us to explore what Microsoft Agent Framework can do for your business.