Microsoft's Agent Framework: What the AutoGen Merger Means for Enterprise AI

Microsoft just made its biggest move in the agent wars: merging AutoGen with Semantic Kernel into a unified Microsoft Agent Framework, with GA targeted for Q1 2026.

This isn’t just a product announcement—it’s a signal of where enterprise AI is heading. Let’s break down what this means for teams building production agent systems.

The Merger Explained

AutoGen and Semantic Kernel have been Microsoft’s two approaches to AI orchestration:

The merger combines AutoGen’s sophisticated multi-agent patterns with Semantic Kernel’s enterprise-grade foundations.

What’s Coming

Based on Microsoft’s announcements and documentation previews:

Unified API Surface

# New unified approach (preview)
from microsoft.agents import Agent, Team, Orchestrator

researcher = Agent(
    name="researcher",
    model="gpt-4o",
    skills=[web_search, document_reader]
)

analyst = Agent(
    name="analyst",
    model="gpt-4o",
    skills=[data_analysis, chart_generator]
)

team = Team(agents=[researcher, analyst])
orchestrator = Orchestrator(team, strategy="hierarchical")

result = await orchestrator.run("Analyze Q3 market trends")

Key Features

• Multi-language support: Write agents in Python, C#, or Java
• Azure-native: Deep integration with Azure AI services, KeyVault, managed identity
• Production SLAs: Enterprise support, uptime guarantees
• Observability: Built-in tracing, metrics, Azure Monitor integration
• Human-in-the-loop: First-class support for approval workflows

The Enterprise Play

This move makes strategic sense for Microsoft:

graph TB
    subgraph "Microsoft AI Stack"
        AF[Agent Framework]
        SK[Semantic Kernel Skills]
        AOI[Azure OpenAI]
        ACS[Azure Cognitive Services]
        AAD[Azure AD]
    end

    subgraph "Enterprise Integration"
        M365[Microsoft 365]
        DYN[Dynamics 365]
        PBI[Power BI]
        TEAMS[Teams]
    end

    AF --> SK
    AF --> AOI
    AF --> ACS
    AF --> AAD
    AF --> M365
    AF --> DYN
    AF --> PBI
    AF --> TEAMS

Microsoft is betting that enterprises want agents that:

1. Integrate seamlessly with existing Microsoft infrastructure
2. Meet compliance requirements out of the box
3. Scale with enterprise support behind them

What This Means for Teams

If You’re Using AutoGen

Current AutoGen code will continue to work through a compatibility layer. However:

• New features will land in the unified framework first
• Migration path is clear but not trivial
• Timeline: 12-18 months of parallel support, then deprecation

If You’re Using Semantic Kernel

You’re better positioned. Semantic Kernel forms the foundation of the new framework:

• Existing skills port directly
• Plugins architecture is preserved
• Breaking changes minimal for core patterns

If You’re Evaluating Options

The merger creates both opportunity and risk:

Opportunity: Microsoft’s commitment signals long-term support Risk: Vendor lock-in to Azure ecosystem deepens

The Lock-In Question

Here’s the uncomfortable truth: the Microsoft Agent Framework will work best on Azure. Period.

Yes, you can run Semantic Kernel against OpenAI directly. Yes, the framework is technically open source. But the integration depth with Azure services—identity, secrets, monitoring, scaling—creates practical lock-in.

For teams that:

• Already committed to Azure: This is great news
• Multi-cloud or cloud-agnostic: Evaluate carefully
• Self-hosted requirements: Look elsewhere

What’s Missing

Despite the impressive feature set, gaps remain:

Durability

Microsoft’s framework inherits Semantic Kernel’s execution model: primarily stateless with manual checkpointing. For long-running workflows:

# You'll need to handle this yourself
async def research_workflow(query):
    # Step 1 - no automatic state persistence
    research = await researcher.run(query)
    # If we crash here, research is lost

    # Step 2
    analysis = await analyst.run(research)
    # Same problem

    return analysis

There’s no equivalent to Temporal’s durable execution or event sourcing. For mission-critical workflows, this remains a gap.

Portability

The framework deeply assumes Azure primitives. Self-hosting means reimplementing:

• Secret management (Azure KeyVault replacement)
• Identity (Azure AD replacement)
• Monitoring (Azure Monitor replacement)
• Scaling (Azure Container Apps replacement)

Where DuraGraph Fits

This is where DuraGraph’s approach differs fundamentally:

Teams building on DuraGraph can integrate Microsoft’s agents as execution nodes while maintaining durability guarantees the framework itself doesn’t provide.

The Bigger Picture

Microsoft’s consolidation reflects industry maturation. The experimental phase of “let’s see what agents can do” is transitioning to “let’s make agents reliable in production.”

But Microsoft’s solution optimizes for their ecosystem. For teams needing:

• Self-hosted deployments
• Multi-cloud flexibility
• Durable execution guarantees
• LangGraph compatibility

The answer lies in infrastructure that treats these as first-class concerns, not Azure add-ons.

Timeline

Resources

• Microsoft Agent Framework Preview
• Semantic Kernel Documentation
• DuraGraph Migration Guide