Semantic Kernel Meets AutoGen: Unified Agent SDK Guide

Building reliable agent systems today often means choosing between powerful but different ecosystems: Semantic Kernel (SK) for structured plugins and enterprise-grade orchestration, and AutoGen for flexible, Python-first multi-agent collaboration. The good news is the industry is converging on common patterns, and Microsoft is aligning its agent tooling across SK, AutoGen, and cloud runtimes. This post explains what a "Unified Agent SDK" really means in practice, how the pieces fit together, and how to migrate or interoperate without rewrites.

What is the Unified Agent SDK

There is no single public package named "Unified Agent SDK" across Semantic Kernel and AutoGen today. Instead, the idea is a practical unification of concepts, tooling, and runtime expectations:

• A consistent agent model (roles, tools, memory, and orchestration)
• Shared interfaces for tools (function calling, plugins, and OpenAPI/MCP)
• A common runtime story for production (observability, safety, and governance)

Semantic Kernel provides the Kernel, plugins, and an Agent Framework across C#, Python, and Java (SK overview, Agent Framework). AutoGen provides AgentChat, Core, and Extensions for scalable multi-agent systems in Python (AutoGen docs). Microsoft Foundry Agent Service introduces a managed runtime that connects models, tools, and orchestration with enterprise controls (Foundry Agent Service). Together, these form a de facto unified SDK surface, even if the packaging remains distinct.

In practice, that means you can treat SK as the place to define durable tools and orchestration primitives, and treat AutoGen as the place to compose agents into flexible conversation-driven workflows. The "unified" part is the shared contract: a tool is a tool, a message is a message, and an agent is an autonomous component with instructions, memory, and the ability to call tools.

"Agents can send and receive messages, generating responses using a combination of models, tools, human inputs, or other customizable components." — Semantic Kernel Agent Framework documentation

Why it Matters

Interoperability is now the difference between a demo and a platform. Teams often start in one ecosystem and later need another for scale, governance, or multi-agent patterns. A unified SDK direction matters because it enables:

• Portability of tools: SK plugins can be exposed via OpenAPI or MCP so that other frameworks can call them.
• Shared agent patterns: planner-reasoner-act loops, tool selection, and multi-agent delegation look similar across ecosystems.
• Lower migration costs: teams can keep business logic and swap orchestration layers.
• Production readiness: managed runtimes like Foundry Agent Service standardize observability, safety, and deployment.

In short, you can invest in durable capabilities (tools and policies) rather than in a single framework surface.

Migration becomes a strategy rather than a rewrite. For example, a team might build tools as SK plugins for strong typing and dependency injection, then adopt AutoGen for rapid multi-agent experimentation. Later, the same tools can be hosted in a managed runtime that enforces safety and auditing. The change is primarily orchestration, not business logic.

Architecture / How it Works

Think of the unified SDK as a layered architecture rather than a monolithic package:

1. Tool Layer: Functions, plugins, or tool definitions with clear schemas and descriptions.
2. Agent Layer: Single or multi-agent orchestration, message passing, and role definitions.
3. Runtime Layer: Security, tracing, evaluation, and resource management in a production environment.

Some teams also separate control plane and data plane concerns. The control plane includes orchestration, policies, and evaluation. The data plane includes tool execution, state, and retrieval. That split lets you move orchestration between SK and AutoGen while keeping tool execution stable.

Semantic Kernel emphasizes a central Kernel that manages services and plugins. Plugins encapsulate callable functions and can be imported from native code, OpenAPI, or MCP (SK plugins). AutoGen emphasizes agent runtimes (AgentChat/Core) and extension points for tools, models, and distributed workers. Foundry Agent Service provides a hosted runtime and SDK surface for enterprise production.

Tip: If you want future-proof interoperability, define tools with clear schemas and expose them via OpenAPI or MCP so both SK and AutoGen can call them without rewriting business logic.

Step-by-Step Example

Below is a minimal cross-ecosystem workflow. We build a tool in Semantic Kernel (C#) and call similar behavior in AutoGen (Python). The point is not a direct runtime bridge, but a shared tool contract and agent behavior.

Step 1: Define a tool in Semantic Kernel (C#)

using System.ComponentModel;
using Microsoft.SemanticKernel;

public class TicketPlugin
{
    [KernelFunction("create_ticket")]
    [Description("Create a support ticket with a title and priority")]
    public string CreateTicket(
        [Description("Short title for the ticket")] string title,
        [Description("Priority: low, medium, high")] string priority)
    {
        // Replace with real persistence logic.
        return $"TICKET-42: {title} ({priority})";
    }
}

var builder = Kernel.CreateBuilder();
// Add model and services here (Azure OpenAI, OpenAI, etc.)
var kernel = builder.Build();

kernel.Plugins.AddFromType<TicketPlugin>("Support");

Step 2: Mirror the tool contract in AutoGen (Python)

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    agent = AssistantAgent(
        name="support",
        model=OpenAIChatCompletionClient(model="gpt-4o"),
        system_message=(
            "You can create support tickets. Use the tool contract: "
            "create_ticket(title, priority)."
        ),
    )

    result = await agent.run(task="Create a high priority ticket: build fails on CI")
    print(result)

asyncio.run(main())

Step 3: Unify with a shared tool contract

• Define the tool schema once (OpenAPI or MCP).
• Register it in SK as a plugin and in AutoGen as an extension.
• Keep orchestration logic separate from business logic.

This approach lets you swap orchestrators without rewriting the tool itself.

Migration checklist when moving between ecosystems:

• Keep tool names and parameter schemas stable.
• Standardize on a single prompt and tool description style.
• Separate agent instructions from tool logic.
• Add telemetry hooks early so you can compare behavior across frameworks.

Best Practices

• Design tools as stable contracts with explicit input/output schemas.
• Keep orchestration thin; put business logic behind tools or plugins.
• Use SK Kernel middleware or AutoGen Core hooks for logging and tracing.
• Prefer small, focused tools to improve function selection accuracy.
• Test agent workflows with deterministic prompts and mocked tool responses.
• Add a compatibility layer that maps tool schemas into framework-specific wrappers.
• Treat agent prompts as versioned assets so rollbacks are easy.

Common Mistakes

• Encoding business logic inside prompts instead of tools.
• Creating huge tool lists that confuse model selection.
• Mixing orchestration and execution (hard to migrate later).
• Ignoring observability until after deployment.
• Assuming one framework will cover every production requirement.
• Failing to pin model versions during migration and blaming the framework.

Real-World Use Cases

• Customer support automation: SK plugins expose ticketing APIs, AutoGen coordinates multiple agents (triage, summarize, respond).
• DevOps incident response: AutoGen handles multi-agent workflows while SK plugins wrap internal runbooks and infrastructure APIs.
• Enterprise search + actions: SK plugins manage retrieval and actions, while a multi-agent AutoGen layer delegates tasks across domains.
• Content operations: SK offers structured tools for CMS actions; AutoGen coordinates review, compliance, and publishing agents.
• Finance ops: SK plugins encapsulate ledger and ERP calls while AutoGen agents coordinate reconciliation and exception handling.

Conclusion

Semantic Kernel and AutoGen are converging on the same agent fundamentals: tools, orchestration, and runtime governance. There is no single "Unified Agent SDK" package yet, but the ecosystem is already interoperable when you treat tools as shared contracts and keep orchestration modular. Start with stable tool schemas, layer in SK or AutoGen orchestration as needed, and plan for a managed runtime when you go to production.

If you are evaluating what "unified" means for your team, focus on contracts, not packages. Build tools once, choose the orchestration engine that best fits your workflow, and make observability a first-class requirement from day one. That approach gives you room to evolve without rewrites as the SDK landscape continues to converge.

For deeper reading, explore the Semantic Kernel Agent Framework and plugins documentation, the AutoGen AgentChat/Core docs, and Microsoft Foundry Agent Service guidance for production runtime patterns.