- AI search work gets you into the answer. Agentic distribution is the extra work so an agent can also call your product and finish a task.
- Content teams can own the prompt set, the public story, examples, and the copy inside skills and tool descriptions. Engineering has to own the interfaces, login, sessions, rate limits, and billing.
- I'd start by measuring the prompts your buyers already use, fixing whatever those answers get wrong, then watching an agent try one complete task and using the failures as the next round of work.
Most of the AI search work we do with clients stops at a pair of questions: does the model mention you, and does it recommend you? That's still the right place to start. We've written about the difference between those two scores, what your library actually teaches the models, and how third-party pages talk about you when your own site doesn't.
Agentic distribution is the next layer of that same program. A buyer (or a coding agent sitting in their repo) isn't only asking what to use. They're trying to use it. The work below is what has to be true for that second part.
Some of it is content. Some of it is product. You don't need every item this quarter. You do need to know which pile each item belongs in.
Getting into the answers that matter
You still have to show up for the jobs you actually sell.
That means two things at once.
The public record: docs, GitHub, comparison posts, community threads, a Wikipedia page if you have one. Models still pull from the same places your buyers already read. If that record is thin or stuck on an old category, you won't be in the shortlist. We treat those off-site URLs as part of the AI search program, not as a later PR project.
Your own site: robots, sitemaps, and the usual files that tell crawlers they're allowed to fetch a clean copy. If the important pages are a JavaScript shell, or bots are blocked, you can do a lot of content work and still not appear.
Then measure it the way we already measure recommendations, not mentions. Write the prompts your buyers would type. If the real motion is someone in Cursor saying "add search to this app," that prompt set is not the same as "what's the best vendor for X." Re-run it. If you're missing from those answers, I'd fix that before you staff a new interface project.
Making sure the model has the right product
If a model only has your public pages, it should still describe the product you actually sell. We see this go wrong all the time when older pages outnumber the live story. What AI search demands from your web content is mostly about that problem.
Headings should name the job. There should be examples. It should be obvious who the product is for. An llms.txt file only helps if it carries that same detail. If it just restates the homepage, fix the pages and then regenerate the file.
Publish a machine-readable API description (OpenAPI or the equivalent) so a caller isn't guessing paths. Put AGENTS.md and CLAUDE.md in the repos you care about so "how we use this here" is written down. If you want that guidance in more than one coding environment, ship it as a skill (SKILL.md). Name SDK methods the way you'd name buttons in the UI: obvious, and don't rename them every quarter.
If you can, let an agent hit a real endpoint before anyone creates an account. That's often faster for the agent to learn from than another docs page.
For technical products, the docs site and SEO/GEO for technical content are usually the same pile of pages. Plan them together.
Giving agents a way to use you
Meet the agent where it already runs. That usually means more than one interface, listed in the places agents already look, with login that works when nobody is sitting in Chrome.
The surfaces we see buyers actually use:
- CLI
- MCP
- Agent skills
- REST
- SDK
- The website itself: readable HTML, markdown copies of the important URLs, and WebMCP if you want the agent to operate the page instead of only reading it
Register those in the directories and package indexes agents search. Write tool names and descriptions the way you'd write a page title: specific enough that the next call is obvious.
Test login as a script. If the only path that works is a person clicking through a consent screen, automated clients will stop there.
When a call fails, the response should say what was missing or where to go next. An unauthorized status with no detail is hard for anything that isn't a person to recover from.
Completing a real task
Listing an interface doesn't tell you whether an agent can finish the job. Have one try to open a project, run the core action, and read status.
The gap I see most often is setup: API keys, webhooks, environments. If those calls don't exist, a person still has to finish the install. You need the admin and lifecycle endpoints, not only the feature endpoint.
Machine-payable APIs only belong on the list if you actually expect an agent to buy without a human. If you don't, leave them off for now. They shouldn't jump ahead of recommendation and identity work unless payment is part of the agent's job.
Some steps should still require a person. Write those down so the team doesn't assume every path is unattended.
Treating agent traffic like its own audience
If you can tell agent sessions from human ones in your logs, do that. Different clients call you differently. When one of them keeps asking for a method you don't ship, that's usually a naming problem or a docs gap.
Rate limits built for a person clicking can block a legitimate agent or let a retry loop keep going. Set limits that match automated use, and detect abuse without treating every agent as an attack.
Send those failures to whoever owns the interface and whoever owns the docs. That's the operating rhythm: the next sprint comes from what broke last week.
We already talk about this a bit in the WebMCP piece: agent sessions look different, and you'll want a baseline before that traffic is large.
Who does what
On the content side:
- The prompt set and recommendation tracking
- Third-party pages that currently describe you
- Identity on the URLs agents already land on
- Examples, AGENTS.md / skill copy, and tool descriptions
- llms.txt and markdown versions of those URLs
On the product and engineering side:
- CLI, MCP, SDK, OpenAPI, WebMCP
- Login that works without a browser
- Whether a session survives a real task
- Lifecycle APIs, rate limits, payments
- Logging by client
Content shouldn't own whether an MCP session stays up. Engineering shouldn't own whether you appear in ChatGPT. Those are related programs. They still need different owners.
Where I'd start
You don't have to do the whole list this quarter. You do have to keep "we show up in the answer" and "an agent can finish the job" as two separate tracks.
- Baseline the prompts that match your buyers, including coding agents if that's the motion.
- Fix the public pages, and the third-party pages you can influence, that currently misstate the product.
- Publish a machine-readable contract and, if you can, one call that doesn't require an account.
- Add the interfaces those agents already use, and test them the way an agent would.
- Confirm one end-to-end task completes, including setup.
- Turn whatever broke into the next docs and interface work.
If you need a smaller first project before this becomes a program, I wrote about how to get started with agentic marketing in that narrower sense: add agents to the system you already have, instead of boiling the ocean.
Frequently asked questions
What is agentic distribution?
The work of showing up when an AI agent is choosing a product, then making it possible for that agent to call you and finish a task. It includes AI search, plus interfaces, login, and the operational stuff around automated traffic.
How is this different from AEO or GEO?
Answer engine optimization (AEO) and generative engine optimization (GEO) are about whether you appear in answers, and how you're framed. Agentic distribution keeps that work and adds the product surface: docs an agent can follow, interfaces it can call, and a task it can complete.
Do we need an MCP server?
If your buyers' agents already speak MCP, put it on the list. It's one interface. It doesn't replace recommendation, identity, or a working install path.
Who should own this?
Content owns the story and the prompt set. Engineering owns the interfaces and whether they hold up. One "AI SEO" owner is not going to be able to debug a dropped session.
How do we measure it?
Re-run a fixed prompt set for recommendation. Then have an agent try one real task. Published files are a prerequisite. They're not the whole metric.
What should we do first if we have nothing yet?
Measure the prompts. Read your own site the way an agent would. If the product story is wrong, fix that before you staff a new interface.
Do we need agents to be able to pay us?
Only if you expect them to buy without a human. Otherwise leave it off the near-term list.
Is llms.txt enough?
No. It's one file sitting on top of the rest of the site. It only helps if the underlying pages already state the product clearly, and if crawlers can fetch them.
Related reading
- What's the difference between being mentioned by an LLM, and recommended? How to read mention scores vs. recommendation scores when an answer engine talks about your brand.
- What's WebMCP, and what do marketing teams need to know about it? What it means for an agent to operate the page itself, not only call an API.
- Agentic marketing: "How do I really get started?" A smaller on-ramp if you need a first project before you staff the whole program.
