The Microsoft Agent Framework, released in October 2025, provides a unified platform combining Semantic Kernel and AutoGen to support the development of production-grade agentic (systems capable of performing complex, multi-step tasks autonomously) applications. The framework focuses on four primary technical domains to ensure robust operation.

Safety is addressed as an empirical measurement problem rather than a theoretical concern, using dual-model comparison to tune guardrails. To connect agents to external tools, the framework utilizes the Model Context Protocol (MCP), a universal adapter that allows agents to interact with data sources without requiring modifications to backend service code.

Workflow management is structured into three specific patterns: sequential, concurrent, and human-in-the-loop, where an AI generates a draft and pauses for human review. Finally, the framework introduces a method for Agentic RAG (a technique that retrieves data from external documents to provide context to an AI model), which uses handoff orchestration to route queries to specialized agents rather than relying on a single retrieval pipeline.

The Microsoft Agent Framework, released in October 2025, provides a unified platform combining Semantic Kernel and AutoGen to support the development of production-grade agentic (systems capable of performing complex, multi-step tasks autonomously) applications. The framework focuses on four primary technical domains to ensure robust operation.

Safety is addressed as an empirical measurement problem rather than a theoretical concern, using dual-model comparison to tune guardrails. To connect agents to external tools, the framework utilizes the Model Context Protocol (MCP), a universal adapter that allows agents to interact with data sources without requiring modifications to backend service code.

Workflow management is structured into three specific patterns: sequential, concurrent, and human-in-the-loop, where an AI generates a draft and pauses for human review. Finally, the framework introduces a method for Agentic RAG (a technique that retrieves data from external documents to provide context to an AI model), which uses handoff orchestration to route queries to specialized agents rather than relying on a single retrieval pipeline.