October Highlights: Your AI Engineering Roadmap.
AI Agents Foundations series. Evals series. NEXUS panel on AI Engineering.
It’s been a busy few weeks here at Decoding AI Magazine!
We’re halfway through our AI Agents Foundations series, and a lot has happened—from building agent loops from scratch to hosting guest posts from industry experts.
To make sure you haven’t missed anything, we’ll have a quick recap on everything published so far in October and give you a sneak peek at what’s coming next.
October Highlights
AI Agents Foundations Series
A 9-part journey from Python developer to AI Engineer. Made by busy people. For busy people.
Everyone’s talking about AI agents. But what actually is an agent? When do we need them? How do they plan and use tools? How do we pick the correct AI tools and agentic architecture? …and most importantly, where do we even start?
To answer all these questions (and more!), we’ve started a 9-article straight-to-the-point series to build the skills and mental models to ship real AI agents in production.
Catch up on any lessons you’ve missed here:
Lesson 1: AI Workflows vs Agents: The Autonomy Slider
Lesson 2: Context Engineering: 2025’s #1 Skill in AI
Lesson 3: Structured Outputs: The Silent Hero of Production AI
Lesson 4: Stop Building AI Agents. Use These 5 Patterns Instead.
Lesson 5: Tool Calling From Scratch to Production: The Complete Guide
…but we weren’t the only ones writing.
We also had some fantastic guest writers contribute to Decoding AI Magazine.
Hugo Bowne-Anderson’s articles focus on a simple but powerful idea: build a solid evaluation framework from day one. This mindset shift toward quality is how you avoid “proof-of-concept purgatory” and launch with confidence.
You can dive into his framework with these posts:
1. Escaping POC Purgatory: Evaluation-Driven Development for AI Systems
A real-world framework for escaping the “PoC Purgatory” and turning that impressive AI demo into a production-ready system. Read more.
2. Stop Launching AI Apps Without This Framework
How to actually build your robust evaluation framework with synthetic data—before your users become your QA team. Read more.
3. The Top 11 Ways to Easily Improve Your AI Applications
Eleven common mistakes teams make when evaluating AI systems, from chasing generic metrics to skipping error analysis. Read more.
Building reliable systems through evaluation is one side of the coin. The other? Making sure those systems stay resilient when things inevitably break.
That’s where Peter Kraft comes in. His article on building reliable AI Agents tackles the operational reality of running AI agents in production, where failures aren’t an edge case, they’re a guarantee. Read more.
What’s Next?
LLMs and Agentic AI in Production - Packt Nexus 2025
And speaking of what’s next, I’m excited to share that I’ll be presenting at the Packt Nexus 2025 virtual summit on November 20–21! The event is packed with speakers from Google, Microsoft, and LangChain—all focused on practical, production-ready solutions.
It will be an honor to share the stage with Maxime Labonne and Chip Huyen on our latest ideas on fine-tuning, building and scaling AI systems.
Also, we have some huge news. Maxime and I we’ll start working on the 2nd edition of the LLM Engineer’s Handbook. In this event we will provide a quick sneak peek on what we are going to add.
I'd love to see you there! You can register online and use my code, Paul50, to get 50% off.
The AI Agents Series Continues...
The series isn’t over. We still have four more deep dives coming your way, on:
Planning: ReAct & Plan-and-Execute
Writing ReAct From Scratch
Memory
Multimodal Data
See you next week.
What’s your opinion? Do you agree, disagree, or is there something I missed?
Enjoyed the article? The most sincere compliment is to share our work.
How can I help
Join the waitlist for our latest course on Agentic AI Engineering, where you will learn to design, build, evaluate, and deploy sophisticated, production-grade AI agents and workflows. Done with Decoding AI’s builder mentality, the course consists of 30+ lessons, with code and theory, during which you will build a real-world AI product that you can show off during your interviews. Done in partnership with Towards AI.
The course will be released in late November.
Images
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so much fun working with you on these, Paul!
Purgatory... Interesting🤔.