This is a guide to putting AI to work inside a company without losing control of it, and it gets revised instead of reprinted. Twelve chapters, free, no account. Agents I built read the field every day. I decide what makes it in.
Why it exists
I set out to write a book called The Ultimate Guide to Agentic AI. Then I looked at how long a book takes to reach a shelf against how fast this field moves, and the whole idea collapsed. A book like that is finished roughly a year before anyone reads it. The best ones on the shelf right now were written before most of what they describe existed. The subject narrowed after that, from agents in general to the decisions a company actually has to make about them, which is the work I do and the only part I can write about honestly.
So this is the version that does not go out of print. The structure of the work changes slowly and the twelve chapters change slowly with it. The back matter changes constantly. Revising a chapter costs nothing. Reprinting a book costs a year.
How it is built
The guide runs on a five stage pipeline. Agents handle four of them.
- Monitor. Pull from the sources worth watching: X, YouTube, Reddit, GitHub releases, vendor changelogs, Hacker News.
- Screen. Score everything against what the guide already says. Most of it repeats something, oversells something, or dies on inspection.
- Draft. Write the revision for whichever chapter the surviving item affects.
- Review. I read it. This is the gate nothing skips.
- Publish. Build, deploy, regenerate the narration for anything that changed.
Chapter four explains that architecture properly, and chapter seven explains how the knowledge behind it is stored and retrieved. If you want to know how the machinery works, those chapters are the answer and this site is the working example.
It is also a demonstration of itself
The guide argues for a way of building these systems. This is how the guide itself is built.
Agents run four of those. The one they cannot run is the one that decides what you see.
Its own register entry
Chapter twelve asks you to keep a register: one row for every automation that touches company data, with six columns. It would be poor form to ask that of a reader and not publish my own. This is the pipeline above, in the format the chapter specifies.
Reads published material about this field every day and drafts revisions to whichever chapter it affects.
Jeremy Quinn
Public sources on a list I maintain, and the current text of the guide. Nothing else on the machine, and no account of mine.
Draft files in one folder. It cannot publish, deploy, or send anything.
Public. No internal or restricted material goes near it.
A bad draft sits in the folder until I read it. The failure mode is my wasted time, not a wrong page. I would notice at the review step, which every revision passes through.
10 August 2026
Seven lines, and they answer every question anyone has asked me about this thing. That is the argument for the register in chapter twelve, made at a scale of one.
I have not found another one like it
Living documents exist. Sites written by AI exist. What I have not seen is a structured guide on this subject whose maintenance is an agent pipeline, whose revision history is published next to the chapters, and whose own architecture is explained inside one of them.
If you know of another, I would like to see it. That is not a rhetorical flourish. This is a format I am figuring out in public and I would rather learn from someone who got further.
What is honest about it
Everything below is true and I would rather you hear it here than work it out yourself.
- Agents draft. I edit. Not every sentence here started with me. Every sentence here was approved by me, and I answer for all of it. If something is wrong, that is mine, not the model's.
- The narration is a clone of my voice. Cloned from a recording of me, generated on every revision. It is genuinely my voice and it is genuinely synthetic.
- The chapters are opinionated. Where the field disagrees, I pick a side and say why. Where I am uncertain, I say that instead of hedging quietly.
- I am not a career software engineer. I direct builds, review what comes back, and own the judgment. That is the method rather than a gap I am hiding, and it shapes who this guide is written for.
Who is writing this
Jeremy Quinn. Two decades in enterprise storage and infrastructure, including running the team that took FreeNAS to TrueNAS. These days I run agent systems in production: an always on operations agent on hardware I own, storage administration over MCP, a market analysis platform, and the pipeline that maintains this guide.
I work with companies on the same problems the chapters cover. Which work belongs to a machine and which does not. What an agent should be allowed to touch. How you know it worked. Whether anyone is still using it thirty days later.
Working together
I take a small number of engagements at a time, usually starting with a short assessment: what work is actually happening, where AI belongs, where it does not, and what each fix is worth. If that turns up something worth building, we build it.
If the guide is useful to you, or wrong somewhere, or you want to talk about the work, the address below reaches me.
jeremy@jeremyquinn.net