Microsoft Agent Framework

What it does

Microsoft Agent Framework provides agent and workflow abstractions for Python and .NET, with multi-provider model support and integration patterns for hosted, enterprise environments.

Best for

• Teams building across Python and .NET
• Enterprise workflows using Microsoft cloud services
• New projects evaluating a supported successor to AutoGen

Before you use it

Choose the smallest orchestration surface that meets the workflow. Validate provider-specific behavior, identity configuration, and deployment requirements in the official documentation before committing to an architecture.

Where it fits

The framework is relevant to organizations that need common agent concepts across Python and .NET, especially when workloads already depend on Microsoft identity, Azure services, or hosted model infrastructure. Its provider and protocol integrations can reduce custom glue, but they do not eliminate differences in model behavior, authentication, quotas, deployment regions, or service-level policies.

Begin with one business workflow and map every external dependency: model endpoint, identity, tool host, state store, telemetry system, and approval channel. Build a minimal vertical slice before designing a broad multi-agent platform. This exposes whether the framework’s abstractions match your runtime and whether the operational model works with existing deployment and compliance controls.

Migration and interoperability

Teams evaluating it as a successor to AutoGen should inventory the concepts used by their current system rather than assuming a drop-in migration. Compare agent configuration, message types, tool registration, termination rules, group coordination, memory, and observability. Preserve behavioral tests around the old workflow, then migrate one path at a time and compare task success, cost, latency, and intervention rate.

MCP and A2A support can help connect tools or other agents, but each connection creates its own trust boundary. Record who operates the remote component, what data crosses the boundary, how identity is propagated, and which operations are reversible. Use narrow credentials and require explicit approval for consequential actions.

Production evaluation

Test both Python and .NET examples in the exact hosting environment you plan to use. Verify managed identity or secret rotation, transient-failure retries, cancellation, state recovery, deployment packaging, and trace redaction. Confirm which components are stable and which are preview before making long-term compatibility commitments.

Choose this framework when cross-language Microsoft integration and enterprise deployment patterns materially simplify the application. Choose LangGraph for graph-centered durable orchestration, or OpenAI Agents SDK for a smaller Python abstraction centered on tools, guardrails, handoffs, and tracing.