A Common-Sense Guide to AI Engineering • Jay Wengrow & Kris Jenkins • GOTO 2026

This interview was recorded for the GOTO Book Club. #GOTOcon #GOTObookclub
http://gotopia.tech/bookclub

Jay Wengrow - Author of “A Common-Sense Guide to AI Engineering” & CEO of Actualize @JayWengrow 
Kris Jenkins - Lifelong Computer Geek and Podcast Host @krisajenkins 

Check out more here:
https://gotopia.tech/episodes/434

RESOURCES
Jay
https://x.com/jaywengrow
https://github.com/jaywengrow
https://www.linkedin.com/in/jaywengrow
https://www.commonsensedev.com

Kris
https://bsky.app/profile/krisajenkins.bsky.social
https://twitter.com/krisajenkins
https://www.linkedin.com/in/krisjenkins
https://github.com/krisajenkins
http://blog.jenkster.com

Links
https://youtu.be/p-R7Doigqzc
https://youtu.be/R7Dv2h3tYCU
https://youtu.be/u1nuS5MUFfc
https://youtu.be/fROpZ6EYa54
https://youtu.be/3a0WHZVb1Gg
https://youtu.be/SOz66dcsuT8
https://youtu.be/MznD2DzlQCc
https://youtu.be/N53Gsz0Gm4c

DESCRIPTION
In this GOTO Book Club episode, host Kris Jenkins sits down with Jay Wengrow — founder of coding bootcamp Actualize and author of the bestselling Common-Sense Guide to Data Structures and Algorithms — to dig into his latest book, A Common-Sense Guide to AI Engineering. Jay demystifies how AI agents actually work: at heart, they're a clever hack where your code intercepts an LLM's text output, watches for special notation, and triggers real functions when it spots them. From there, the conversation expands into guardrails (regex, judge LLMs, and specialist ML models), multi-agent architectures for complex tasks, and a hands-on example of a 150-line podcast-generating app built entirely from scratch — no framework required.

The real throughline is a pragmatic, sceptical take on the current AI tooling landscape. Jay argues that frameworks can lock you into patterns that haven't been proven yet, and that the field is too new to know which abstractions are genuinely worth having. His rule of thumb: reach for a framework only when it will do something meaningfully better than you can — not just faster. The book was deliberately written around fundamentals rather than specific tools, so it ages well even as the ecosystem moves at breakneck speed. The conclusion is refreshingly grounded: understand the LLM's inherent limitations, build the middle layer thoughtfully, and don't outsource your system prompts to anyone — or anything.

TIMECODES
00:00 Intro
03:43 How AI agents actually work: The clever hack under the hood
09:56 Multi-agent systems: When one LLM isn't enough
15:40 Frameworks vs building from scratch
23:26 Writing a book for a fast-moving field
25:24 Outro

RECOMMENDED BOOKS
Jay Wengrow • A Common-Sense Guide to AI Engineering • https://pragprog.com/titles/jwpaieng
Jay Wengrow • A Common-Sense Guide to Data Structures and Algorithms • https://amzn.to/4bPiTjd
Jay Wengrow • A Common-Sense Guide to Data Structures & Algorithms in Python • https://amzn.to/3PpwtlT
Jay Wengrow • A Common-Sense Guide to Data Structures and Algorithms in JavaScript • https://amzn.to/4dDSZBl

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