Which Scaffolding for Your AI Agent?

We’ve all been amazed by what LLMs can do. But the real work begins after the demo, when you have to make an AI do something useful. That means giving it tools, connecting it to data, and building scaffolding around it so it can reliably perform tasks. This is the world of AI agents.

And as soon as you decide to build one, you face a choice. It’s a choice that says a lot about what you’re building and who you are. The choice is between something like Microsoft’s Semantic Kernel and OpenAI’s own Agent SDK.

Looking at them, you might think they’re direct competitors. They both help you build agents. But that’s like saying a freight train and a Formula 1 car are competitors because they both have engines. They’re engineered for fundamentally different jobs.

Semantic Kernel: The Freight Train

Semantic Kernel is Microsoft’s answer to building agents. And it’s exactly what you’d expect from a company that has powered large enterprises for decades.

It’s built to be robust and to work everywhere. It supports C#, Python, and Java. Why? Because if you’re a big company, you don’t have the luxury of everyone using the same language. You have teams in different decades of technology. A tool has to meet you where you are.

It’s also mature. It’s past version 1.0, which is a quiet promise of stability. It says, “build your business on this; we won’t change everything next month.” For a large organization, predictability isn’t boring; it’s a critical feature. Semantic Kernel is designed for a world where systems are complex, security is paramount, and downtime is expensive. It’s the freight train: powerful, reliable, and designed to haul serious cargo across a vast network.

OpenAI Agent SDK: The Formula 1 Car

Then there’s the OpenAI Agent SDK. It’s built for one thing: speed.

It’s Python-only. For a small, AI-focused team, this isn’t a limitation; it’s a simplification. It’s also described as “evolving.” For an enterprise, that’s a red flag. But for a startup trying to build with the absolute latest OpenAI tech, it’s exactly what you want. You get the newest features—the improved function calling, the built-in search—the moment they are ready, straight from the source.

Using it feels like you’re working with a direct line to the model architects. The goal isn’t to be everything to everyone. The goal is to be the fastest and most direct way to build with OpenAI’s technology. It’s the Formula 1 car: unbelievably fast, highly specialized, and not intended for hauling groceries.

It’s a Choice of Philosophy

So the question isn’t “which framework is better?” The real question is “what kind of team are you?”

Microsoft’s philosophy is to build for organizational complexity. They build for the Fortune 500. Their tools are designed to be integrated, not to stand alone.

OpenAI’s philosophy is to build for the frontier. They are a research lab at heart, and their tools are for others who want to live on that frontier with them. They accept the churn in exchange for being at the bleeding edge.

If you work in a large organization, especially one with a heavy .NET presence, your choice is practically made for you. You need the stability and breadth of Semantic Kernel.

If you’re a small team building an AI-native product, and you’ve bet your future on OpenAI’s models, you’d be crazy not to use the tools they built for you. You take the speed and agility of the Agent SDK.

You see this fork in the road everywhere in technology. The broad, stable tool versus the sharp, fast one. When it comes to building AI agents today, your choice of scaffolding depends entirely on the shape of what you’re building. Choose the one that fits.

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