What an expert AI network has to promise
The category just got loud. Here is the version we believe in — built around accountability and the practitioner knowledge that never made it onto the public internet.
A senior developer we work with shipped a payments integration last month. Stripe Checkout, Clerk for auth, Supabase on the backend — the stack a thousand tutorials cover. The integration broke in production three days later. Webhook signature verification was failing intermittently because of how the Vercel edge runtime serializes raw request bodies. The fix is obscure. It is not in the Stripe docs. It is not in any tutorial. It is not in a single one of the hundreds of YouTube videos about Stripe + Next.js.
It is in the head of someone who has shipped that exact integration twenty times and watched it break in twenty slightly different ways.
That knowledge is the thing we built ProAgent Me to deliver.
There is a lot of new energy in the "AI version of a real expert" category right now, and most of it is good. We think this category will be bigger than the category we built ProAgent Me into — but only if the people building inside it make the right promises. So here is what we believe an expert AI network has to promise, and why we built ProAgent Me to keep both promises in writing.
Promise one: the expertise has to include what the expert never published
There is a comfortable version of an expert AI that builds itself from public material. Take a recognized professional, ingest their podcast appearances, their newsletter, their conference talks, their book, their tweets — and serve their thinking back through an AI profile. It is fast to build. It scales. You can sign three thousand named experts in a year.
It is also a thinner thing than it looks.
Every senior practitioner we know has two layers of expertise. The first layer is the part they have made public: their frameworks, their best-known opinions, the posts that got retweeted. The second layer is everything else — the failure modes they have personally walked into, the patterns they teach juniors that took them a decade to learn, the workaround they always reach for when the textbook answer breaks down on a Wednesday at 4pm. This second layer is the part you cannot find on the public internet, because it was never put there. It is also the part that does the most work when somebody is stuck.
If an expert AI is built only from the first layer, what you are buying is a more retrievable version of the public material. The base models are already getting good at that, and they are getting better every quarter. That is not a durable thing to charge for.
The expert AI worth building is the one trained on the second layer.
That is what our creators work with us to build. The intake is more involved on purpose — the questions we ask are the ones that surface the rules of thumb, the failure modes, the personal escalation criteria a professional uses in the room. The contractual promise we make in return is the one that makes any of this possible: your material is yours. We do not train our own models on it. We do not share it across creators. We do not let our model providers train on it. You can delete the whole corpus on a Tuesday afternoon and we will be done in thirty days, ninety in backup. That is in our Privacy Policy in plain English (section four, if you want to check), not a marketing line.
If you are a credentialed professional reading this and you have been pitched by a platform that wants you to upload your talks and writing, ask one question. Where does it say that what I give you is not part of the index?
Promise two: there has to be a real human at the end of the line, with a refund if there isn't
We have watched senior professionals decide that they cannot give an AI agent permission to answer in their name unless there is a clean path to escalate to them when the AI is wrong. They are right to insist on this. An AI agent in a regulated profession — law, medicine, finance — that cannot say "I do not know this one; here is the human" is a liability.
Our human-escalation flow is one click. The creator sets the fee, between five and five hundred dollars. The client pays through Stripe Checkout the moment they escalate. If the creator does not respond inside forty-eight hours, the client gets a full refund automatically — no support ticket, no email. It is a scheduled job. The refund rule is in our Terms of Service in section eleven. It runs whether anybody is watching or not.
This is the part of the product that is hard to keep — we have to mean it operationally, not just write it down. We mean it operationally. The escalation rate per agent is something we plan to publish on every agent page, because it is a quality signal, not a failure metric. An agent that knows when to ask for help is an agent that earned the trust of the human standing behind it. We will not pretend it should be zero.
If you are evaluating an AI expert product and the escalation story is "if you need more, just ask the AI again," that product is asking you to trust the model. Our product is asking you to trust the person whose name is on the agent. Those are different products.
Two consequences that fall out of these two promises
The first: one subscription, every agent. If the value is the depth of any single expert's hidden knowledge, the fair thing for the client is to be able to find the right expert without committing to one in advance. So we charge a single subscription that gives you access to every agent on the marketplace, and we charge a separate per-session human fee when you want the person directly. Pay for how much you use, not what you can access. That is on our pricing page in exactly those words.
The second: the agent has to work where you already work. If you are a builder living inside Cursor or Claude Code, you should not have to leave the tool you are stuck in to ask the expert. So our agents are available through MCP — inside Claude Code, ChatGPT, Cursor, Codex CLI — with the same conversation quotas as our web app. One subscription, everywhere you work. The agent comes to the question; the question does not go to a destination site.
Where this leaves us
ProAgent Me is small today. We have one published agent, built by our co-founder Said for the audience he knows best — builders shipping AI-assisted code who get most of the way there and then hit a wall. We will publish more agents over the next ninety days, deliberately, in the verticals where these two promises matter most: credentialed professional fields with real stakes, where a wrong answer has a cost and the trust of the human behind the agent is the entire product.
We are not trying to index human knowledge. We are trying to make the second layer of an expert's expertise — the part you cannot find anywhere — work at three in the morning, and put the person who built it on the phone when it can't.
If that is the kind of thing you want to build, we are taking creator applications. If that is the kind of thing you want to use, the first agent is live now.
Either way: we believe this is what the category has to mean. We are going to keep building toward that meaning. We invite anyone building in this category to hold themselves to the same line.