Discover how Microsoft’s two key frameworks, Semantic Kernel and AutoGen, provide a powerful stack for creating sophisticated multi-agent AI systems. This article explores how to build specialized agents with Semantic Kernel and orchestrate their collaboration with AutoGen, while also comparing this approach to leading alternatives like CrewAI and LangGraph.

I recently finished reading “Unlocking Data with Generative AI and RAG” by Keith Bourne, and it provides a comprehensive overview of building systems that can leverage large language models (LLMs) with your own private or new data. This post summarizes my key takeaways from the book.

The core idea is that the key technical components of a Retrieval-Augmented Generation (RAG) system include the embedding model that creates your embeddings, the vector store, the vector search mechanism, and the LLM itself.

Engineering managers operate at the intersection of numerous information streams, leading to significant context-switching overhead and potential loss of critical information. This post outlines the design for a context-aware AI assistant, orchestrated by the Gemini CLI, to solve this problem. We will explore two solutions: a short-term workaround using a command-line bridge and the recommended long-term solution involving a dedicated local server.