Using Semantic Kernel for Complex Agentic Workflows

Imagine a developer building a customer support bot that can handle basic questions but completely falls apart the moment it needs to check a shipping status or look up a previous order. This is the wall most teams hit when moving from simple LLM prompts to actual autonomous agents. This post explores how to use Semantic Kernel to manage these complex, multi-step workflows through structured orchestration.

What is Semantic Kernel?

Semantic Kernel is an open-source SDK from Microsoft that allows developers to integrate LLMs like GPT-4 into conventional programming languages like C# or Python. It acts as the glue between your raw code and the reasoning engine of an AI. Instead of just sending a string to an API, you're building a system of "skills" or "plugins" that the AI can actually execute.

It's more than just a wrapper. It provides a way to manage state, handle memory, and—most importantly—orchestrate functions. If you've ever struggled with an LLM hallucinating a function call, you'll appreciate how this framework brings structure to the chaos.

How do you build agentic workflows?

You build agentic workflows by defining specific plugins that the kernel can call based on the context of the conversation. This transforms a simple chatbot into an agent capable of taking action.

A typical workflow follows this pattern:

1. The Planner: The kernel analyzes the user's intent.
2. The Selection: It picks the right plugin (e.g., a database lookup or an API call).
3. The Execution: The code runs the specific function.
4. The Feedback Loop: The result is fed back into the LLM to determine the next step.

If you're worried about the latency introduced by these multiple loops, you might want to look into optimizing LLM latency with prompt caching to keep things snappy. It makes a huge difference in user experience.

Semantic Kernel vs. LangChain

While both frameworks aim to solve the same problem, they cater to different developer preferences and ecosystems.

The choice usually comes down to your existing stack. If your team is already living in the .NET ecosystem, Semantic Kernel feels like a natural extension rather than a foreign library. It treats AI as a first-class citizen within a structured, typed environment (which, let's be honest, is much easier to debug than pure prompt engineering).

When building these agents, remember that the goal isn't just to "talk" to an AI—it's to build a system that can do things. Use the kernel to bridge the gap between natural language and your existing business logic.