If you built agentic workflows on Semantic Kernel, the 2026 path forward is Microsoft Agent Framework: Microsoft calls it the "direct successor" to Semantic Kernel and AutoGen, built by the same teams. It keeps Semantic Kernel's enterprise features, adds graph-based workflows with human-in-the-loop state, and lets you migrate step by step by reusing existing kernel functions as tools.
Updated October 2026
Who this is for: .NET and Python engineering leads, Azure architects and CTOs at Microsoft-centric enterprises (banking, insurance, manufacturing, public sector) with Semantic Kernel or AutoGen code in production or pilot.
The business problem: an agent codebase on a moving platform
Many teams wrote their first agents in 2024–2025 on Semantic Kernel (SK) or AutoGen. In 2026 they face a choice: keep building on a framework whose successor has shipped, or spend engineering time migrating. Delay means new features land elsewhere; a rushed rewrite risks breaking agents that already touch customers or finance systems. The goal is a migration that is incremental, tested and tied to a business release, not a big-bang rewrite.
What changed in 2026
- Microsoft Agent Framework (MAF) "combines AutoGen's simple abstractions for single- and multi-agent patterns with Semantic Kernel's enterprise-grade features such as session-based state management, type safety, filters, telemetry, and extensive model and embedding support."
- MAF supports .NET, Python and Go; the Go SDK is in public preview with some features not yet available.
- Microsoft published an official Semantic Kernel migration guide (updated August 2026), including a compatibility layer that reuses SK functions as MAF tools (requires semantic-kernel 1.38 or higher).
- Microsoft's Azure Architecture Center standardised five orchestration patterns: sequential, concurrent, group chat, handoff and magentic.
Note: the Semantic Kernel overview still describes version 1.0+ support across C#, Python and Java with a commitment to non-breaking changes. We have not seen an end-of-support date; check Microsoft's release notes before you plan around one.
Semantic Kernel vs Microsoft Agent Framework
| Concept | Semantic Kernel | Microsoft Agent Framework |
|---|---|---|
| Core object | Every agent depends on a Kernel | No kernel; create an Agent from a chat client |
| Tools | Plugins with @kernel_function | Plain functions or @tool passed to tools= |
| Agent types | ChatCompletionAgent, AzureAIAgent, OpenAIAssistantAgent | Agent (Python) / ChatClientAgent (.NET), plus Copilot Studio and A2A agents |
| Multi-step orchestration | Agent group chat, process framework | Functional and graph-based workflows with explicit execution paths |
| Languages | C#, Python, Java | .NET, Python, Go (preview) |
| Tool protocol | Plugins, OpenAPI | Functions plus MCP clients and hosted MCP tools |
Mappings are taken from Microsoft's migration guide and MAF overview. Map SK process-framework code to MAF workflows case by case; the SK migration page does not cover it.
Reference architecture: agentic workflows on the Microsoft stack
Teams, web app, API, events
Graph of agents + functions, checkpoints for humans
Model client + tools (functions, MCP, legacy SK functions)
Dynamics, SAP, SQL, SharePoint
Figure: An agentic workflow on Microsoft Agent Framework. Requests arrive from channels, a graph workflow routes them through agents and plain functions with human checkpoints, agents call enterprise systems through tools, and a shared operations layer handles models, telemetry, identity, policy and cost.
A low-risk migration plan
- Inventory: list every SK agent, plugin, planner and process, and which business process it serves.
- Wrap, don't rewrite: use the compatibility layer to expose existing kernel functions as MAF tools so business logic stays put.
- Move the orchestration: rebuild agent chats and processes as MAF workflows, starting with sequential flows.
- Prove parity: replay the same test cases through old and new; compare accuracy, latency and cost.
- Cut over per workflow with a rollback switch, not per codebase.
Microsoft's own advice applies throughout: "If you can write a function to handle the task, do that instead of using an AI agent." Fewer agents means fewer things to migrate and test. For loop design inside each agent, see the agentic reasoning loop; for pattern selection, see agentic workflows.
Sitting on Semantic Kernel or AutoGen code? Get a free migration sizing review: we inventory your agents and give you a phased plan with effort estimates. Request the sizing review.
Where it's heading: 2023 → 2030
| Period | Microsoft agent stack |
|---|---|
| 2023–2024 (history) | Semantic Kernel reaches 1.0 across C#, Python, Java; AutoGen popular in research. |
| 2025 | Teams combine SK and AutoGen; Microsoft Agent Framework introduced as the unified successor. |
| 2026 (now) | MAF for .NET and Python, Go in preview; graph workflows, MCP tools, official SK and AutoGen migration guides. |
| 2028 (expected) | Most new Microsoft-stack agent work on MAF; cross-vendor agent calls over A2A. |
| 2030 (our view) | Framework choice matters less than the runtime, governance and observability around it. |
How to start in 30 days
- Week 1: Inventory SK/AutoGen code and rank workflows by business value and risk.
- Week 2: Port one low-risk workflow to MAF using the compatibility layer; keep tools unchanged.
- Week 3: Add tracing and a regression test set; run old and new side by side.
- Week 4: Cut over, document the pattern, and schedule the next three workflows.
How NexaStack helps
Framework code is only part of production. NexaStack is a unified control plane to deploy, operate and govern agents across private cloud, on-premises and edge, with a Unified Inference Engine for open and hosted models, an Observability and Evaluation Layer, Agentic Identity Management and Policy, Safety and Governance controls. That lets Microsoft-stack agents run next to sensitive data, including self-hosted and air-gapped environments. Read the pillar, what is agentic AI, plus AgentOps and the AI agent infrastructure stack.
FAQ
Is Semantic Kernel being replaced?
Microsoft positions Agent Framework as the direct successor to Semantic Kernel and AutoGen and publishes migration guides. Check Microsoft's release notes for SK support dates before planning.
What is the difference between Semantic Kernel and Microsoft Agent Framework?
MAF removes the Kernel object, uses plain functions as tools, adds graph-based workflows and supports .NET, Python and Go (preview).
Can I reuse my Semantic Kernel plugins?
Yes. Kernel functions can be exposed as Agent Framework tools through a compatibility layer (semantic-kernel 1.38 or higher), so you can migrate gradually.
How much does migration cost and what is the risk?
It depends on how many agents and processes you have. Wrapping existing functions and migrating one workflow at a time with side-by-side tests keeps both cost and outage risk low.
What are the latest Semantic Kernel and agent framework trends in 2026?
Consolidation into Microsoft Agent Framework, graph workflows, MCP tool access, A2A agent calls and stronger telemetry and evaluation.
Migrate one Semantic Kernel workflow to production-grade in 30 days. Book a NexaStack demo and migration sizing call. Book a demo.
