Experts are saying that Agentic AI is the next big thing in the AI world. I thought of riding the wave and wrote 2 programs today ([1] A financial agent and [2] a RAG agent). Soaking every bit of it and enjoying the same 😄 Here's Microsoft CEO's take on this topic:

The course consists of 10 lessons that cover the fundamentals of building AI agents. Each lesson stands alone, so start wherever you like! ✨ [Link to the course in comments!] Here's what it covers: 🏁 Intro to AI Agents 🔍 Agentic Frameworks 🧠 Agentic Design Patterns 🔨 Tool Use Design Pattern 🔗 Agentic RAG 🛡️ Building Trustworthy AI Agents 🗺️ Planning Design Pattern 🤝 Multi-Agent Design Pattern 💭 Metacognition Design Pattern 🚀 AI Agents in Production

Dear fellow Data Alchemists, I am creating a database for column-based data analysis and another database for pdfs and documents for agents. I have been recommended ClickHouse and neo4j respectively. 2 questions I needed some insight on. 1 - Are these the best options for performance keeping costs in mind? Would love to hear the community's views. 2 - If there are any recommendations for alternatives, please share. Thank you!

When building AI agents, you need them to understand your data—whether it’s PDFs, websites, or internal documents. Most tools for this are closed-source, requiring API keys and external platforms. But what if you could do it all in Python with an open-source library? In this week’s video, I show you how to build a fully open-source document extraction pipeline using Docling. You’ll learn how to: - Extract, parse, and chunk documents for AI processing. - Store and retrieve data efficiently with vector databases. - Build a working chat application that can answer questions based on your documents. Watch the video here.

A 6 weeks interactive course to learn how to build and deploy your own agents. Register for free 👇🏼https://huggingface.co/agents-course