Build Smarter AI Workflows with Gemini + AutoGen + Semantic Kernel

Author(s): MD Rafsun Sheikh

Originally published on Towards AI.

Source: https://medium.com/ai-simplified-in-plain-english/exploring-microsofts-autogen-framework-for-agentic-workflow-254b50620699

“Any sufficiently advanced technology is indistinguishable from magic.” — Arthur C. Clarke

AI isn’t just about answering questions anymore — it’s about building smart agents that can collaborate, specialize, and solve real-world tasks. In this tutorial, I’ll walk you through how I combined Google’s Gemini Flash with AutoGen and Semantic Kernel to create a multi-agent AI assistant capable of everything from analyzing text and summarizing reports to reviewing code and generating creative solutions.

This isn’t just theory — you’ll see working code, how to structure your prompts, and how to deploy everything step-by-step.

We’ll be working in Python, so let’s start by installing the core libraries and then import everything we’ll need:

Let’s configure the Gemini API using your API key (grab it from Google AI Studio) And define the model and API settings:

We’ll create a wrapper class to simplify interacting with Gemini:

Here, we define advanced AI functions using the @kernel_function decorator. These will power specific tasks like analysis, summarization, and code review.

Here, we create specialist agents using AutoGen’s ConversableAgent interface.

We’ll wire up everything into the AdvancedGeminiAgent class — now capable of both multi-agent collaboration and semantic task execution.

“Technology is a word that describes something that doesn’t work yet.” — Douglas Adams

Here’s your… Read the full blog for free on Medium.

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Published via Towards AI