Agent Engine Optimization: Making Your Company Machine-Readable

A founder I advise did something last month that would have been unthinkable two years ago. He needed a new analytics vendor, and instead of opening five tabs and booking three demos, he handed an AI agent his requirements and his budget and told it to come back with a shortlist. It did, in about four minutes: three vendors, each with pricing, integration notes, and a one-line reason it made the cut. Then he showed me the ones it had silently dropped. One of them was, on the merits, the strongest fit in the category. It lost because the agent could not read it. Its pricing was a "contact us" form, its specs were trapped in a PDF, its proof points sat behind a gated case study. To a human skimming the website, that company looked perfectly credible. To the machine doing the buying, it did not exist.

That is the shift nobody put on a roadmap, and it is already here. Gartner projects that 90% of B2B buying will be intermediated by AI agents by 2028, routing more than $15 trillion in spend through machine-to-machine exchanges. Buyers are already pulling away from the old playbook: Gartner found that 67% of B2B buyers now prefer a rep-free experience, and 45% used AI during a recent purchase. The front door has moved too: G2 reports that 51% of B2B software buyers now start their research inside an AI chatbot rather than a search engine. The first thing that reads your company is no longer reliably a person. It is a model.

Discovery Got You Found. This Decides If You Get Chosen.

Getting mentioned by an AI is not the same as getting picked by one.

I wrote earlier this year about generative engine optimization, the work of making your company surface when an AI answers a buyer's question. That was about discovery: being in the answer. Agent engine optimization is the harder sequel. It is about being legible enough that an autonomous agent can evaluate you, compare you against rivals, and place you on a shortlist with no human in the loop. Discovery gets you named. This gets you selected. And selection is where the money is, because the agent is not browsing for inspiration. It is narrowing a field down to the two or three vendors a human will ever actually see.

The uncomfortable part is that you can win discovery and still lose here. Plenty of companies have spent the last year getting their content into AI answers, then watched the agent skip them at the evaluation step because the moment it needed real specifics, pricing, integrations, security posture, there was nothing structured to grab.

The Agent Reads Differently Than a Human

A human researcher forgives a lot. An agent forgives nothing.

A person will tolerate a vague pricing page, infer what "enterprise-grade" means, sit through a demo to fill the gaps, and give you the benefit of the doubt because your brand looks established. An agent does none of that. It is parsing for discrete, comparable facts: what does it cost, what does it integrate with, what is the data residency, what is the proof it works. If those facts are locked in a PDF, a slide, a gated asset, or a sales rep's head, they are invisible to the thing making the comparison. I described the agent as a new kind of buyer in The AI Buyer Is Already Here. The follow-on truth is operational: that buyer reads in structured data, and most companies are still writing for the eye, not the parser.

This is why "we have great content" is not an answer. Great prose aimed at a human is exactly the format an agent struggles to extract reliable claims from. The agent does not want your narrative. It wants your facts, cleanly stated and easy to lift.

It Is a Context Problem, Not a Formatting Problem

The instinct is to treat this as a markup task: add some schema, tidy the pricing page, ship it. That helps, and it is not the real fix.

The real fix is that the facts an agent needs are usually scattered across a dozen disconnected places that do not agree with each other. Pricing logic lives in a spreadsheet sales keeps privately. The integration list on the website is six months stale. The security details are in a SOC 2 PDF nobody links to. The positioning in the deck contradicts the positioning on the homepage. A human reconciles all of that on a call. An agent reads the contradiction and downgrades you for it. Making your company machine-readable is not a cosmetic exercise. It is the work of getting your own truth into one current, coherent, parseable form, which is the same operating-layer problem I keep coming back to in Context Is the Whole Game. If your context is fragmented internally, it will read as fragmented externally, and the agent will choose the competitor whose facts line up.

That is the through-line for everything Expona argues. Context is the operating layer, and in an agent-mediated market it is also your storefront. A company that holds its customers, its product detail, and its proof in one connected, maintained layer can expose a clean, consistent version of itself to a machine. A company whose truth is splintered across tools and documents cannot, no matter how good its copywriting is.

The Human Still Closes, But the Agent Picks Who They Meet

None of this means the buyer disappears. It means the buyer arrives later and pre-filtered.

Gartner found that 69% of B2B buyers still turn to sales reps to validate AI-generated insights, so the human conversation is not going away. But notice what that conversation now is: a person confirming a choice the agent already framed. By the time a human is involved, the field has been cut, and it was cut by a machine reading structured facts. You do not get to charm your way onto the shortlist anymore. You get onto it by being readable, and only then do you get the chance to close. Optimize for the agent so you survive to meet the human.

What to Make Machine-Readable This Quarter

You do not need a re-platform. You need to expose your real facts in a form a parser can trust.

Start with the four things an agent always wants and most companies hide: pricing logic, the integration and compatibility list, security and compliance posture, and concrete proof of outcomes. Get each into plain, current, structured text on a public page, not a PDF, not a gated form, not a "contact us" wall. Then run the test that matters: hand your category and your requirements to an off-the-shelf AI agent and watch whether it can describe and compare you accurately. Where it guesses, hedges, or skips you, you have found a gap in your machine-readability. Fix the source of truth behind that gap, not just the page, because the page will drift again if the underlying context stays scattered.

The Takeaway

The buyer's first reader is now a machine, and that machine does not browse, infer, or forgive. It parses for facts and shortlists on what it can cleanly extract. Discovery work gets you into the answer. Agent engine optimization is what gets you out of the answer and onto the list a human actually buys from.

The companies that win the agent-mediated market will not be the ones with the cleverest copy. They will be the ones whose pricing, specs, and proof are legible to a parser because their context is coherent underneath. If your truth is scattered, you read as scattered, and the agent quietly chooses someone else. Make your company machine-readable, starting with the facts you currently make humans dig for.

If you want the weekly take on context, AI, and the operating model of the next decade, subscribe to the blog (below).

Tracy Thayne* is the founder of Expona, an AI-powered operational intelligence platform for B2B marketing. Read the Expona founder story or subscribe to the blog (below) for weekly insights on context, AI, and the operating model of the next decade.*