Semantic Kernel Succeeded by Microsoft Agent Framework as Enterprise-Ready SDK

Microsoft's Semantic Kernel is an open-source SDK for connecting large language models to business systems. It allows developers to create plugins, memory, and orchestration for AI agents. Eight production examples of Semantic Kernel use cases have been identified: multi-agent proposal generation, governed enterprise assistants, natural-language-to-SQL, Copilot Studio skill extension, filtered retrieval over vector stores, stateful orchestration, authentication-context persistence, and per-task model routing.

The most decision-relevant fact about Semantic Kernel in 2026 is that Microsoft has named its successor: Microsoft Agent Framework. The agent workloads survive contact with production at all, and McKinsey's global survey shows that only 10 percent of organizations report scaling agents in any single business function.

Each use case puts a hard boundary between the model and the business system. This boundary is what the McKinsey scaling data implies is missing from most agent programs stuck short of production. Plugins, tool contracts, state machines, filters, and authentication context are all mechanisms for constraining what an agent is permitted to do.

Should a new build start on Semantic Kernel in 2026? If there's no existing codebase, it's recommended to start on Microsoft Agent Framework. For existing Semantic Kernel systems in production, plan the move carefully, and resolve the value question before migrating.