Valuable Resources for Understanding AI Agents: First Principles
AI Agents can only be designed, built and applied well if the underlying components are understood to a sufficient level.
These are the resources that I found the most valuable so far for diving into those first principles, without getting “vendor-locked”. I believe we learn the most when we share our knowledge, so I’m happy to share these with you.
Books
- AI Engineering by Chip Huyen: plus the 76 minutes speed-run video summary by Marina Wyss
Video Courses & Lectures
- Neural networks playlist including how LLMs work, by Grant Sanderson (3blue1brown)
- Deep Dive into LLMs like ChatGPT by Andrej Karpathy
- MIT Introduction to Deep Learning | 6.S191: 10 lectures by Alexander Amini and Ava Amini
- LF Decentralized Trust Belgium Meetup on Trusted AI Agents with Andor Kesselman: plus our Howest Cyber3Lab write-up
Papers & Whitepapers
- Identity Management for Agentic AI by Tobin South for OpenID Foundation
- OWASP Top 10 for Agentic Applications 2026: A globally peer-reviewed framework identifying the most critical security risks facing autonomous and agentic AI systems
Newsletters & Blogs
- AI News by smol.ai: Top AI discords + AI reddits + AI X/Twitter summarized each day
- Context Engineering: Anthropic’s blogpost on the evolution from prompt to context engineering
- Niels Rogge on LinkedIn: Machine Learning Engineer at ML6 & Hugging Face, he does a great job translating technical details of innovations (often newly released models) to what they bring and how they work
Courses
And of course, the courses and sessions offered by my colleagues at HOWEST University of Applied Sciences.
As you can see, I’m learning a ton and having fun! Which resources do you find most valuable?