How to Build AI Agents in 10 Lessons
Microsoft has released an excellent learning resource for anyone looking to dive into the world of AI agents: "AI Agents for Beginners". This comprehensive course is available free on GitHub. It is designed to teach the fundamentals of building AI agents, even if you are just starting out. What You'll Learn The course is structured into 10 lessons, covering a wide range of essential topics including: Agentic Frameworks: Understand the core structures and components used to build AI agents. Design Patterns: Learn proven approaches for designing effective and efficient AI agents. Retrieval Augmented Generation (RAG): Enhance AI agents by incorporating external knowledge. Building Trustworthy AI Agents: Discover techniques for creating AI agents that are reliable and safe. AI Agents in Production: Get insights into deploying and managing AI agents in real-world applications. Hands-On Experience The course includes practical code examples that utilize: Azure AI Foundry GitHub Models These examples help you learn how to interact with Language Models and use AI Agent frameworks and services from Microsoft, such as: Azure AI Agent Service Semantic Kernel Agent Framework AutoGen - A framework for building AI agents and applications Getting Started To get started, make sure you have the proper set-up. Here are the 10 lessons Intro to AI Agents and Agent Use Cases Exploring AI Agent Frameworks Understanding AI Agentic Design Principles Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Multi-Agent Design Patterns Metacognition in AI Agents AI Agents in Production Multi-Language Support To make learning accessible to a global audience, the course offers multi-language support. Get Started Today! If you are eager to learn about AI agents, this course is an excellent starting point. You can find the complete course materials on GitHub at AI Agents for Beginners.
RAG / Vector Database best practices for CoPilot Studio
I have been working with Microsoft Gen AI LLM tools (Azure OpenAI Studio and CoPilot Studio) for building a custom 'agent' for answering questions about a set of company internal documents. It seems like RAG is the best approach and fine-tuning would be overkill. In support of RAG with a vector database I would like to understand best practices. It isn't clear to me if manually uploading files to the CoPilot within CoPilot studio does effective preprocessing of the documents (e.g. tables) and chunking or not. And, if it adds embeddings for words not in the pre-trained LLMs embeddings vocabulary. I am looking for the best practice on an ongoing basis for automating the updates (add,update,delete) to the RAG content on an ongoing basis for multiple additional customized LLMs with different sets of documents. It seems like that leveraging open source technology like "langchain" might be a way to achieve consistent results for LLMs that might be updated on a regular basis with RAG content. Is that advised? Or are there Microsoft tools that might be better for automating content updates? Also, there is a choice of what vector database to use, posgrestsql, Cosmos DB (Mongo), etc. Which are supported and recommended for Copilot studio. Thanks in advance for any guidance!
