microsoft-agent-framework

Multi-agent workflows in .NET use graph-based execution patterns, middleware pipelines, and function tools to orchestrate agents, handle streaming responses, and apply structured outputs for reliable operations.

The best way to implement human-in-the-loop for AI agents is by designing graph-based workflows with checkpointing capabilities, allowing execution to pause and resume based on human input before proceeding further.

Yes, you can integrate OpenTelemetry for observability in .NET AI agents. It enables monitoring coordinated actions, tracking streaming responses, and capturing telemetry data to ensure scalable and reliable agent operations.

To expose an AI agent as an MCP tool, you apply specific deployment patterns and dependency injection techniques within your .NET workflow, allowing the agent's function tools to be consumed by other systems.

Yes, this approach supports graph-based streaming execution with sub-workflows. It allows you to design cooperative workflows, implement robust error handling, and manage complex multi-agent orchestration effectively.