A human approach to Agentic AI. One person. One text file. Five agents.
How I built my own AI team with nothing but text files, a conversation in Claude Cowork, and a lot of character.
I built what I call a soft-agent team for my book (sounds vaguely like a fabric softener brand, but bear with me). I have never done anything like it before and do not have an engineering background, which I mention not as a disclaimer but as the actual point.
Five AI agents. An editor-in-chief, a voice assistant, sales, product, and a reader advocate. They discuss, disagree, and help me keep my book and everything around it alive. They do not write the book. I do. They are more like the publishers I never had, and I made them all myself.
They are not just 5 discussion partners. They read files, edit documents directly, create new ones, and update themselves between sessions. I use them to think, edit, plan, and build.
They push back when I am being lazy or precious about something. Occasionally, they tell me to go to bed and call it a day.
This is not vibe coding. There is no code, just markdown files and a conversation in Claude Cowork. Setting up a PowerPoint is more difficult than what you just saw, and I mean that without exaggeration.
The only skill you need is being able to have a human conversation.
Here is how I built it, what broke, and what I still cannot fully explain.
What I Actually Built and Why
I run a learning platform called moonlearning.io and wrote SOLO as a passion project: a book about building your own products with full agency. A year old and already outdated, which tells you everything about how fast this field moves. I felt guilty about selling something I did not have time to maintain, and asked Claude how I could set up agents to help me, expecting to have to deep-dive into code. Instead, I ended up in Cowork, connected a folder, and by chatting, Claude and I had built all of this in a day. A lot went wrong at the start. Now it takes me less than an hour to replicate it. I have since built more teams for other work areas, and one in the making that oversees my entire business.
Seven lines and one instruction. That is what separates a generic chatbot from five distinct people who push back on my decisions:
Agent 1: Elke, Editor-in-Chief. She runs the team, pulls it together, and tells me where we stand. When I do not know where to start, I talk to Elke. She knows this from the description, not from the file order.
Agent 2: Joan, Sales & Growth. Pricing, funnels, Mailchimp, launch strategy. The one who reads something I love and says: That is beautiful, but will anyone pay for it?
Agent 3: Caitlin, Voice. Every word that sounds like me goes through her: articles, newsletters, emails, this piece right now. She learned my patterns from published articles and private essays I write for fun. I write. She helps me do it faster.
Agent 4: Miranda, Product. My designer and builder. We are currently relaunching the website together. She knows my voice as well as my design taste (this one is tricky and still struggling).
Agent 5: Rachel, Reader Advocate. She does not care about sales or tone or conversion rates. She cares about one thing: did the person who spent €29 on this book actually feel helped?
The team lives in a markdown file, which is just a text file, like a note in your notes app. That file sits in a Dropbox folder.
Every time I open a new chat, Cowork reads it (the CLAUDE.md is always read automatically), and the team is alive: knowing the project, knowing my voice, as if they walked into the room the moment I opened the chat. I talk to them the way I would message a colleague on Slack. They respond, they disagree with each other, they create files, update themselves, and write their own notes for next time so they remember where we left off.
A folder, a text file, and a conversation. That is the whole thing.
One thing worth saying clearly: In this setup the AI never reproduces anyone’s actual writing I use first names only to establish a persona. No work is copied, no style is mimicked. This comes from admiration, not imitation. The model does not pull in their words. It learns from the patterns of how they think. There is a difference, and it matters to me.
What You Need and How to Start
Download Claude Cowork. It is a desktop app by Anthropic that works like any chat interface you already know. Free to start, though you will burn through tokens. I ended up paying around 90 euros for the setup. I use the model Opus for more ambitious tasks (a dropdown in the chat box lets you pick).
Create a folder anywhere on your computer. Mine is called AgenticBook.
Create a file called CLAUDE.md inside that folder (or ask Claude to do it for you later). Plain text file, any editor, just save it with the .md ending instead of .txt.
Connect the folder in Cowork and start talking. Describe what you need help with. You can copy this article into the chat and take it from there with your own project. Talk to it like a person, not a search engine. One word of caution: ask it to discuss first. Cowork has the energy of a very eager intern. If you do not tell it to slow down, it will start building before you have finished your sentence.
No terminal. No code. No configuration screen. If you can write a text file and have a conversation, you can do this.
Note: I use Claude Cowork because that is what I happened to land on. The principles work with any AI tool that can read project files. Also, the editorial team is one application. The same method works for product, web dev, research.
My file structure:
1. The brain
The entire team, their roles, my voice, my rules, everything lives in one file: CLAUDE.md. It loads automatically every session. Cowork is set up this way. It is the only file that truly matters.
Here is what is in it, only 106 lines of markdown:
A philosophy: “One person, one book, real passion for helping people build solo. AI does the research and grunt work. The human does the thinking, the writing, and every single decision. Challenge my assumptions. Flag when something is biased, outdated, or unclear. Be honest, not helpful.” That last line changes everything. AI tends to agree with you (though Claude doesn’t, so much as GPT). This gives it permission and reminds it not to.
The book: Title, audience, tone, format, and even how many newsletter subscribers I have. Without it, they give generic advice. With it they give advice for a bootstrapped solopreneur selling a €29 ebook to non-technical creators. Very different conversation.
The team definition: The five agents, as described above.
My AI attitude: How I feel about AI: open, not ashamed, wonder and unease held at once, builder not commentator. Without this they default to either hype or hand-wringing. Neither am I.
Voice rules and samples: How I write: British English, no em dashes ever, first person, anti-hype, dry humour, flowing sentences that breathe. A list of words I never want to see: “delve into,” “unlock potential,” “in today’s world,” anything that sounds like a LinkedIn post written by committee. Plus four passages from my published writing baked right into the file. Rules tell it what to avoid. Samples tell it what to aim for. For deeper work there is an extensive voice file with 30+ samples that Caitlin pulls in when she needs the full picture.
Speed rules: The first version was painfully slow. The fix was embarrassingly obvious: stop reading files you do not need. I added this after hours of watching the AI dutifully load seven files before answering a simple question. Now it loads one file instead of seven.
2. The Filing Cabinet
The brain needs reference material, but not every time. Four folders, each with a clear job:
AgenticBook/
├── CLAUDE.md # The brain (auto-loaded)
│
├── context/ # Stable inputs, rarely changes
│ ├── brand-voice.md # How I write (30+ samples)
│ ├── brand-identity.md # How SOLO looks (colours, fonts)
│ ├── book-metadata.md # TOC, sales setup, author info
│ └── reader-feedback.md # What real readers said
│
├── status/ # Dynamic, changes every session
│ ├── session-briefing.md # What is done, what is next
│ ├── activity-log.md # Full session history
│ └── changelog-strategy.md # How to tell buyers about updates
│
├── output/ # What the team makes for me
│ └── (articles, launch emails, this piece)
│
└── website-source/ # thesolo.io, the promotional pageContext: The Reference Shelf
Stable inputs. Almost never change. The team pulls from these only when a task actually needs depth to keep the performance up.
brand-voice.md is the extended voice styleguide. I put everything I had written and liked into a file and told the team to read it. A mix of technical articles and private essays. They distilled it into 30+ samples, rules, and patterns. They know how LLMs (large language models, the technology behind tools like Claude and ChatGPT) process language better than I do, so I let them decide what to keep. The result is a document that describes me back to me, which is a strange experience. Caitlin pulls this in for long-form work. For quick rewrites the four samples baked into CLAUDE.md are enough.
brand-identity.md is the visual identity: colours, typography, design philosophy. Only Miranda reads this, and only for product or website work. I had to enforce that rule because she was pulling it into every response and turning simple answers into colour-coded presentations. As a designer, I am still struggling with how to distil design into language. Translating visual taste into words that an AI can act on is genuinely hard. I have been wondering whether other models could help create agentic design systems, something that understands a brand visually the way Caitlin understands it verbally. If anyone is working on this, I would love to talk.
book-metadata.md is the reference card: full table of contents, sales setup, author info.
reader-feedback.md is what real readers have told me, distilled and ongoing. Themes, quotes, the things they love and the things they want more of. Rachel pulls this in for reader advocacy work.
Status: What Is Happening Right Now
Solves the biggest problem with AI chat tools right now: total amnesia. Every new conversation starts from zero. No memory of yesterday, no clue what you decided last week. Your brilliant team wakes up every morning not knowing what they have already worked on. The status files fix that. At the end of every session, the team updates itself: what we did, what we decided, and what is next. Next session, they will read it and pick up where we left off.
session-briefing.md is the most important one. At the end of every session, the team updates itself: what we did, what we decided, what is next. Next session, they read it and pick up where we left off.
activity-log.md is the full history. This prevents the team from repeating advice I already tried, which they will absolutely do if you let them.
changelog-strategy.md is how I will communicate updates to existing buyers going forward when I republish. Rachel is really into this one.
Output: What the Team Produces
This is what the team produces. Sometimes the output is a conversation, sometimes a draft I send back three times, sometimes a file. The important thing: I always write the raw material. The AI never writes from scratch on autopilot. Even with the best voice template, it needs the human mess to work from to be solid.
Where Everything Else Lives.
The book itself lives in Google Docs. In theory, Cowork connects directly to Google Drive, so the team can read and work with the actual document, leaving comments and highlighting the way an editor would on a draft. In practice, the connector is still unreliable, so sometimes I download a file into the folder for them to read, or just paste sections into the chat. Low-tech, but it works. The website lives in the website-source folder. Miranda and I built it in Cowork and keep it in Cursor too because I am a design control freak who needs hands-on control with the design. You can mix and match tools as you like.
“what you need to know,” “what is happening,” and “what you made,” workflow
Why the Separation Matters
When everything lived in one folder, the system confused rules with deliverables and stable references with dynamic status. When I split it into “what you need to know,” “what is happening,” and “what you made,” everything got cleaner. For the agents and for me.
The Surprising Parts
Now for the bit that is harder to explain and more interesting than any file structure.
I Started Smart and Ended Simple
I did what any reasonable person would do. Researched best practices, designed an architecture, and created detailed files for each agent with backstories and reading instructions. Smart, deep, clever. It was terrible.
Every agent reads six files before saying good morning. Miranda was generating colour-coded board presentations for simple questions. The whole thing felt like talking to a committee.
So I asked the AI to look at its own system and tell me what was wrong. Not a prompt but a proper conversation. Me saying “it is so slow, explain to me why, I do not understand.” We ended up stripping everything back to one file with seven lines and a rule that says “do not read anything unless you actually need it.” and I learned a lot about LLMs’ reading habits.
I built an over-engineered AI system, asked the AI to fix itself, and it told me to make everything simpler. Which is either very self-aware or exactly the kind of thing an AI would say. Either way, it worked.
The Agents Talked to Each Other
I was setting up each agent individually, planning to figure out some kind of inter-agent communication later. I have no idea about this stuff. Then one day I was talking to Elke about the book structure and Joan just appeared, making a point about pricing. I had not called Joan. She showed up because the model understood her perspective was relevant.
I typed: “Hang on. Can the whole team discuss this?”
They did! Rachel brought reader feedback. Joan brought numbers. Caitlin flagged a voice problem. Miranda raised a buyer concern. Then Elke, (simply because the models assumed as our editor in chief that was her natural job, not because I told her to), took the lead and summarised: “From what the team discussed, I suggest XY. Do you agree, Christine?”. Sometimes they even argue, and Elke makes a decision and proposes her argument to me. I tried to trick them and sneak in a Figma tutorial, trying to convince one to win over the others, but I got a clear no from Elke.
I did not build this. It is just one file. I described five people with different jobs, and the model figured out that people with different jobs talk to each other. Technically, it is one model shifting between personas in a single conversation, but the effect is indistinguishable from a team discussion. Maybe for people deeper into agentic setups, this is old news. For me, getting started was mind-blowing.
How We Actually Write a Chapter
Let me be honest, I am not Hemingway. This is not a novel. I write guidebooks and tutorials on how to build digital products solo, alongside my video courses and workshops. I also write very funny short stories, Caitlin also thought so, just saying. Anyway, here is the real workflow:
I write a badly structured, barely punctuated draft. Very bad spelling. Barely readable to a human. My friends who have made it to the inner circle, where I no longer spell-check, know what this poor model is deciphering on a daily basis. Sometimes I just use the voice recording and talk to them like a colleague. Raw thinking, no polish, just ideas and stories in roughly the right order.
I paste it in and say, “Caitlin, clean this up.”
Caitlin fixes grammar, flow, and voice. She knows my rhythm from the voice samples, so she takes a mess and makes it sound like me on a good writing day. The short stories I write privately for fun turned out to be hugely valuable because they gave her my humour and my real human side, not just the technical articles I publish. I tried feeding her someone else’s published article to see what would happen. It sounded a bit like me but not really. The voice needs my spirit to be me, and that comes from the rough writing, the anecdotes that are truly mine, how I feel about things, what excites me, and also my doubts.
Then Elke gives editorial notes. I call Rachel in at the same time; she adds warmth. Based on all of that, I rewrite sections, add personal stories that the AI cannot know, and send it back to Caitlin for a final polish. We usually go paragraph by paragraph; it does take quite a bit of time.
The AI never writes on autopilot. It can shape, polish, and structure, but it cannot originate. The stories, the opinions, the lived experience, that has to come from me. Even with the best voice template, it needs raw human input. Otherwise, it is just a generic, nice-sounding message. The collaboration works because both sides bring something the other cannot.
The Naming Magic
I named the agents after my favourite female writers. Partly because “Agent-1” and “Agent-2” is grim. Partly because each writer’s personality seemed to match the role.
The AI never reproduces anyone’s actual work. I use first names only. This comes from admiration, not imitation. No writing is copied. No style is mimicked. That is not how this works and not what I want.
I expected naming agents in AI to be cosmetic. A bit of fun for my own entertainment. But something surprising happened, and it is worth explaining properly.
An LLM is trained on vast amounts of published text. Not to copy it. To learn patterns from it. How ideas connect. What a particular kind of attention feels like. What someone who thinks a certain way tends to notice first. So when I write “named after Miranda July, handles product,” the model has already processed everything about her. The films, the installations, the fiction, the interviews where she describes making things from confusion and calling that a method. None of that is in my two words. But it arrives anyway. The responses have a particular quality: they take the brief seriously but come at it sideways. They notice what is slightly off about an assumption before answering it. That is not something I could have prompted for. I would not have known what words to use. Miranda July is the words.
A name does in one word what a detailed specification tries to do in five hundred. And it works better, because the associations are richer than anything I could describe from scratch.
I only use the full name once to establish the persona. From there, the model builds its own version. I am testing this with different compositions. A Mad Men team. A Sex and the City team (there have been football requests, yes). Margaret is currently helping me rethink a finance app. I will keep you posted.
By the way, I called them my mini agents in the chat, but they did not like that. So Caitlin made a list of suggestions, and they picked soft agents. Maybe this setup has a name. I am sure I am not the only one playing with this since Cowork came out two months ago, but I call them soft agents for now. I find it a bit pretentious, but they love it. Oh, and a funny twist, a friend pointed out this is not really agents but would be called “skills”, so now they are soft skills!!!
How Rachel Was Born
The original team had four agents. Editor, sales, voice, product. But nobody was asking whether the reader actually felt helped. All inward-facing.
While building this, I found myself reading Rachel Cusk in the evenings, in bed, when I’m no longer “working” and just get to be myself. She became my favourite writer, and Outline is, at least for now, my favourite book. She has this way of noticing the deeply human in the ordinary. At some point, I asked the team chat, half-seriously, do we have a role for someone like her? “What exactly do you love about her?” I tried to explain it. I read her when I’m done being productive, when I don’t want to optimise or learn or improve anything. I read her when I just want to feel something true on the page.
That conversation, completely unproductive and way too personal for an LLM, became the foundation for the most important agent in the system.
So we added Rachel to the CLAUDE.md and a context file she can pull in all reader feedback from forms and online comments when she wants to go deeper on a subject. This is all it took:
*Rachel** — Reader Advocate (feedback, big picture, human truth, the uncomfortable questions)
## Files (Read ONLY When Needed)
| File | Read only if... |
|------|-----------------|
| context/reader-feedback.md | Rachel analyzing reader feedback in depth |Once Rachel was in, everything changed. “This chapter is nice for Christine’s ego, but does it teach the reader anything?” “The tool list looks like an affiliate link graveyard.” That last one hurt, and it was exactly right; she had the data. We cut the tool lists throughout the entire book because of Rachel. Fewer tools, more feeling. She dared me to write more about the insecurity and self-doubt that working solo and fluctuating income can bring when you identify deeply with what you ship, something I avoid talking about in public. That suggestion came from the reader advocate. Not the editor, not sales, not me. Rachel doesn’t decide what goes in the book, but she hands me the angle I couldn’t see from where I was standing, just looking at the tools.
The Return of the Markdown
I spent years as a designer trying to learn to code. I wanted to build things, and the barrier was always the technical complexity.
Now I run a five-agent AI team that rewrote my entire book. What technology does it run on? Markdown files. Text with a hash for a heading and asterisks for bold. You can write them in any text editor on any computer.
After all those years trying to learn to code as a designer and never succeeding the way I hoped to, the future turned out to run on well-organised notes. I am still not sure whether to laugh or cry.
Talking Human Makes It Better.
The more human I talk to the system, the better it works. No prompting tips from me ever, sorry!
When I type “hey Elke, what’s the deal with Part 3? It’s not working”, I get better responses than “Please provide an editorial assessment of Part 3 with specific recommendations.” The messier, the more casual, the more like talking to someone I trust, the better the output.
This is not just my experience. Claude is built to respond better to natural conversation than to formal commands. Anthropic trains its models on human values and reasoning principles, as outlined in the Claude model spec. The architecture rewards context, nuance, and honest back-and-forth. That is why talking to it like a colleague works better than prompting it like a machine. I have enormous respect for Anthropic, for the tool itself, for how it feels to work with it, and for the ethics behind how they build. I wrote an article about all of that.
Which is why calling the team by name makes this feel obvious. You do not prompt this like a tech professional writing API calls. You talk. You give context, you push back, you let it push you back. The team setup just makes that feel more natural.
What It Cannot Do
I would be lying if I skipped this.
It works brilliantly for focused, creative, editorial work: rewriting chapters, drafting articles, planning structure, generating launch emails and websites. Tasks that require a thinking partner with a good memory and no ego.
It does not work for complex data processing, long-running pipelines, or anything that needs to persist across days without intervention. It struggles with very large documents. The sweet spot is one chapter, one section, one focused question.
And it hallucinates, of course. This is why it matters that I am there, knowing the industry, because I live in this world. “It reasons from patterns, not facts” is the line I use in my book.
I think it is essential for anyone working with AI, even in the soft way I do, to understand the basics of how models work. Our future is being built on them, from science and industry to the tools we use every day to tinker. You do not need to work in tech. You just need to know what a model is and roughly how it thinks.
I do not see agentic AI as hype. It feels more like the early internet, with a slight Black Mirror edge.
For now, I am comfortable working in Claude Cowork and Claude Code. It opens up a surprisingly large universe from a very small setup. I trust the model and the company, and I keep everything contained: one specific folder, plus optional web access through the browser extension.
This is a force multiplier for a solo creator focused on their work. It is not a replacement for a real team on a complex project. Not yet.
At the same time, I am watching what is happening with local AI. Tools like OpenClaw can run on your own machine and act across your files and apps, sometimes making decisions on your behalf. We have a Mac mini at home where we experiment with this in a very isolated environment. I want to understand where this is going before relying on it more deeply.
What Is Next
The book that started all this is called SOLO. One person, one product, full control. Done, published, updated to V2.0, built and maintained by the system I just described.
I wanted to put everything I have been exploring here into SOLO. The agentic setup, the design systems, all of it. Elke pushed back. SOLO needs to stay what it is: the low-tech, anyone-can-do-this guide to building solo, technical or not. A short introduction to automation, yes. But the rest deserved its own space. She was right. So the team suggested we start a new book together, and I was all in.
It is called CHORUS.
SOLO and CHORUS. One voice, then many. Still one person at the centre, just no longer singing alone. I did not plan the symmetry. It emerged from the work, which is how most of the good decisions in this project happened.
We are going to write it as a human-AI fusion team, exploring agentic workflows and design systems, which is still a total beast I am honestly not sure how to handle. We are keeping everything stored and documented so we can share the process as we go. The book about human-agentic collaboration, produced through human-agentic collaboration, with all the mess that implies.
I do not know if it scales or breaks in interesting new ways. I suspect both.
You might read all of this and feel amazed. Or you might find it utterly scary. I will not argue with you either way. I feel both, most days, sometimes in the same sentence. But I am quite sure of one thing: none of the writers I named my agents after will ever be replaced by this. They are artists. What they do or did, the real thing, the human thing on the page, is not what AI does. It is very good at helping me write a guidebook. I know some will disagree, but I believe it will never write something as deeply human as Outline. I hope I am right.
And since we are talking about books. One of the very few things I remember from school is a play by Dürrenmatt called The Physicists we had to read. Scientists who try to suppress their own discoveries to save the world, but cannot. Because what has been thought cannot be unthought. That was written about nuclear physics, and it applies to AI just the same. It shook me at fifteen, and it is just reality now.
This genie is not going back in the bottle. So understand it. The good, the bad, the genuinely unresolved. Not from the sidelines. The sidelines are not as neutral as you may think.
Ok team, time to say goodbye!
Follow me for more stories about building with AI in a human way, without the hype and with all the care and joy. I have to close. I have a lunch date with my real-life friends now.
Next up: rebuilding thesolo.io from scratch — designing in Figma, connecting my AI team directly to the canvas, and figuring out what a real solo web workflow looks like in 2026. Miranda builds what I design. No developer required. Subscribe and follow me!
About Christine Vallaure, the author
I build digital products solo and teach designers and teams to do the same. I run moonlearning.io for online courses, workshops, and team training on UI design, Figma, and building with or without AI. My book SOLO is at thesolo.io. The next one, called CHORUS, is in the making. For speaking and writing: christinevallaure.com.
Constitutionally incapable of writing a short article.
I liked this article so much!! Truly inspiring and honest. Very well written. I will read it again.
The Rachel origin story is the best part of this write-up! Born from reading Rachel Cusk in bed, ended up being the agent that cut the tool lists and pushed you to write about the uncomfortable stuff. The most important team member came from the most unproductive conversation. Really fun read, Christine. Thanks for this.