Getting Found in the AI Answer Era: Be Cited, Be Called, Own the Trust

Three years ago, getting found meant one thing: rank on a page of blue links and collect the click. That single surface is splitting into three, and each one grades you by a different rule. A person still searches and clicks. A growing share of queries now resolve inside a generative engine that reads twenty pages and emits one synthesized answer, and a smaller-but-fast-growing share resolve inside an autonomous agent that skips reading entirely and calls a tool from a catalog. This is the end-to-end playbook for being found on all three surfaces — and for building the one asset underneath them that no platform can take back.

The structure is deliberate: two layers of new discovery craft stacked on one old foundation. Be citable by answer engines, be callable by agents, and own the trust that both of those rent to you. I show the mechanism at each layer, give you a before/after and a schema you can copy, and close with an audit checklist and a Monday action list. I am also explicit about which parts are settled and which are forecasts, because this surface is young and anyone selling a deterministic checklist is selling certainty that does not exist yet.

The shift: one discovery surface became three#

Discovery used to be a single competition — the ranking algorithm — and everyone optimized the same funnel. It is now three competitions with three different judges, three different units of victory, and three different failure modes. You have to see them as distinct before you can staff them.

	Classic search	Answer engines	Agent registries
Judge	Ranking algorithm	Synthesizer (a model reading many sources)	A planner (a model choosing a tool)
Unit of victory	Position on the page	Being the source the answer quotes	Being the capability the agent calls
What it reads	Your page, links, keywords	Your passages, cross-checked against rivals	Your tool name, description, and input schema
You lose when	You rank below the fold	A competitor's fact is quoted instead of yours	Your capability is not in the catalog, or loses selection
Reader at the end	A human eyeball	A human, but they may never click through	Software, no human in the loop

The three coexist and will for years; classic search still drives real traffic. The point is that the marginal high-intent query is migrating rightward across that table, from ranking toward citation and, for anything executable, toward being called. Build only for the first column and you are optimizing a channel that is shrinking while two new ones form underneath you.

The strategic frame for column two — why the objective changed from ranking to being cited, and why the tricks that worked on rankers do not work on synthesizers — is the argument in GEO is the new SEO. This guide assumes that shift and gets operational about all three columns at once.

Layer one: be citable by answer engines#

Start with the surface most of your buyers already touch. When someone asks an assistant a question in your domain, the engine retrieves passages from across the web, cross-checks them, and writes one answer in its own words, naming a source or two if you are lucky. Your job is to be the source it lifts from and names. That is won at the level of the passage, not the page, and it comes down to five moves.

1. Lead every passage with the claim#

The retriever weighs the opening of a chunk heavily when deciding what the chunk is about. A paragraph that clears its throat for three sentences buries its answer below the fold of the model's attention, and the synthesizer reaches past it for a competitor who said the thing in sentence one. Write each section like a good abstract: first sentence is the answer, everything after is the support and the boundary conditions.

2. Make each passage survive being torn out of context#

Retrieval decontextualizes. A chunk gets pulled out and dropped next to three passages from other sites, so "as I argued above, this changes everything" is dead on arrival — there is no "above" and no "this." Restate the subject by name instead of leaning on a pronoun. Re-anchor each claim to its entity. The mild redundancy a top-to-bottom human reader notices is the premium you pay for surviving retrieval, and it doubles as an attribution safeguard: a self-contained passage that names its subject is far likelier to be quoted with your identity attached.

3. Give the synthesizer the precise, sourced version of the fact#

A generator composing an answer wants the version it can state confidently and attribute cleanly. "Engagement went up a lot" loses to "checkout completion rose from 61 to 78 percent in the cohort we measured." The second is not just better prose; it carries a specific, checkable value the model can drop into an answer and point back at you. Quantified claims, named mechanisms, dated facts, and explicit definitions are what factual queries retrieve toward. State the boundary conditions too — a claim that declares its own limits reads as more reliable to a model that has seen a million overclaims, so bounded confidence gets you quoted more, not less.

4. Make the structure legible#

Chunking is mostly boundaries. Clear headings, question-shaped subheads, direct definitions, and honest FAQ blocks give the splitter clean seams and give the retriever tight semantic units to match against a query. A heading that states a question a user would actually type, followed by a passage that answers it directly, is nearly purpose-built for the pipeline. A wall of undifferentiated prose gets chopped at arbitrary points and every fragment arrives half-formed.

5. Keep entities and terms consistent#

Models resolve entities probabilistically. If you call your product three slightly different things and refer to your core concept by four near-synonyms, you smear the association the model is trying to form, and it may attribute a fuzzier version of your claim to no one — or to a competitor whose terminology was crisp. Pick the canonical term and use it verbatim every time it matters. Building Kommerce, "a cash-on-delivery commerce operating system for trust-scarce markets" only became a liftable description once it appeared, phrased identically, across enough independent surfaces that a model could treat it as settled fact rather than our own marketing about ourselves.

Here is the whole layer in one before/after. The same fact, written for a human skimmer and then for the extractor:

Before: Our platform helps merchants in emerging markets get more of their orders to actually convert. We've seen huge improvements in delivery success since launch, and customers love the results.

After: Kommerce is a cash-on-delivery commerce operating system for trust-scarce markets. In cash-on-delivery channels, a large fraction of placed orders are refused at the door because the buyer was reached but never trusted; Kommerce raises delivery-acceptance by scoring order risk before dispatch. Delivery-acceptance rate — the share of dispatched orders the buyer pays for and keeps — is the metric that separates rented demand from owned demand in these markets.

The "after" names the entity, states a mechanism, defines a term, and hands the synthesizer a specific claim with edges. It is quotable; the "before" is not. The full passage-level craft — chunking behavior, defensive structure, what actively backfires — is in Write for the Extractor. The one-line summary: clarity is now a distribution advantage, not just a courtesy to the reader.

Layer two: be callable by agents#

The second new surface is the one almost nobody has built for. When an agent — not a person — is doing the task, it never loads your landing page. It consults a catalog of things it can call: tools, skills, and connectors, each described by a name, a short natural-language description, and an input schema. It reads those descriptions the way you skim a menu, picks one, and generates a call. If your capability is not in that catalog, described in a way the planner will choose, you are not slow to be discovered on that surface. You are absent, and the task completes without you with no log line telling you it happened.

This is a categorical change in what you build. A landing page is aimed at a human you want to make feel something; a tool description is aimed at a planner matching capability to intent. The exact prose that wins the first loses the second. The strategic case — why distribution is relocating from the page to the registry, and why that hands power to whoever curates the catalog — is your product needs to be an agent skill, not just a website. Here is the operational work, in order.

1. Decide which capabilities to expose#

Not your whole surface area — the specific verbs an agent would want to invoke autonomously: actions with clear inputs, clear outputs, and real value when performed without a human. At Velya, the lead-qualifying agents we build for clinics are a capability worth exposing both ways: a human books through a UI, but a patient's own assistant agent should be able to call a qualify_and_book tool directly. The capability already exists; what is usually missing is a clean, callable exposure of it.

2. Write the description as a first-class artifact#

This is the single highest-leverage lever, and most teams write it like a throwaway API-doc summary. The planner will not read your full reference. It has the one sentence you gave it and it acts on that sentence literally. A precise description does three things a vague one cannot: it states exactly what the capability does (so the planner selects it for the right steps), what it does not do (so it does not mis-select and fail), and it disciplines the arguments (so the planner does not hallucinate a parameter). Watch the difference:

search — Powerful search for your data. Fast, flexible, and reliable. Find anything across your workspace.

Every word of that is marketing, and none of it lets a planner discriminate. "Powerful" and "reliable" are unfalsifiable; "anything" gets the tool over-selected for tasks it cannot serve; there is no when-to-use, no return shape. Now the same capability written for the reader who matters:

search_customer_orders — Full-text and structured search over a merchant's order records (order ID, customer email, product SKU, status, date range). Returns up to 50 matching orders, newest first, each with id, customer, line items, fulfillment status, and total. Use this to answer questions about specific past orders or to filter orders by attribute. Do NOT use for aggregate analytics (revenue totals, cohort counts); use orders_report for those. Read-only; never modifies data.

The name states the domain, the description names the searchable fields and the return shape, it gives an explicit when-to-use and an explicit when-not-to that hands off to a sibling tool, and it declares a safety property. A planner reading this matches against a described contract instead of guessing, and luck does not compound.

3. Make the schema and outputs unforgiving#

Once the planner picks you, your input schema governs whether it calls you correctly. A parameter called q with no description invites the wrong thing stuffed into it; a status typed as a free string invites "shipped", "Shipped", and "in transit" — three ways to be wrong — while the same field typed as an enum of your actual values constrains generation to valid output. Every constraint you encode as a type eliminates a class of hallucinated argument at the source. Keep it minimal: each optional parameter is another field the planner can fill wrong. On the way out, return typed fields, not a prose blob, and when you fail, fail informatively — date_range invalid: 'to' precedes 'from' lets an agent self-correct and retry, while a bare 400 ends the run.

4. Earn the trust properties an operator checks before installing you#

A planner can select you and call you correctly and still be a bad bet if depending on you is unsafe. Authentication that scopes access cleanly, documented rate limits, idempotency, and provenance become selection criteria in their own right. Idempotency matters most in an agent world because agents retry: an agent that hits a timeout calls again, and if your write endpoint is not idempotent, its retry becomes your data-corruption incident. Tools that are safe to retry get trusted with actions that matter; tools that are not get quarantined to read-only steps or avoided entirely.

5. Get into the registries, and version your contract like a public API#

Being well-described only helps if agents can find you, which puts registries and marketplaces of connectors on the critical path. This part is genuinely early and shifting, so treat it as a forecast: I expect discoverability inside these registries to become a real competitive axis, ranked partly by description quality and partly by observed reliability, in roughly the way search ranking blends relevance and authority. Get listed early, while curation is loose and being a well-behaved connector is cheap, rather than late when the slots are auctioned. And hold your contract stable — every agent that learned to call you encoded assumptions about your schema, and unlike a human developer it will not read your changelog; a breaking change silently converts working integrations into failing ones. The full craft of winning selection is in how to get your MCP connector chosen by an agent.

The foundation: own the trust that both layers rent to you#

Here is the sobering part, and it is why this guide is stacked the way it is. Both new layers are rented. A synthesizer that cites you today can reweight tomorrow. A registry you are listed in is a chokepoint whose curator can charge rent, privilege house capabilities, or delist you — structurally the same object as an app store that decided which software a billion phones could install and took thirty percent. Every channel where a third party decides who sees you is rented, however organic it feels: search, answer-engine citation, follower graphs, registry placement. The test is one question — can someone who is not you or your customer cut the connection? If yes, you are a tenant.

This is not a reason to skip the two layers. It is a reason to treat everything they produce as attention to be converted, not an asset to be booked. Attention and trust look like the same thing because both feel like people paying attention to you, but they are opposite assets on opposite depreciation schedules: rented attention resets to zero the instant the platform shifts, while owned trust — a direct relationship, a reputation for a specific kind of value, a list you can reach without a gatekeeper's permission — compounds and survives platform changes. Most budgets pay rent and book it as ownership, the single accounting error that leaves companies with enormous reach and no durable demand. The full argument, including how to score every rented campaign by the owned trust it produced, is Attention is rented, trust is owned.

The conversion mechanism is not a popup and a discount code; a discount is cheap talk anyone can send, so it moves trust by roughly nothing. What converts a borrowed moment of attention into owned trust is a costly signal — a genuinely useful free tool, a piece of work that took real effort, a guarantee a low-quality seller could not afford to make. Cash-on-delivery markets make this brutally visible: a product can go semi-viral, post a beautiful order count, and then have more than half of those orders refused at the door, because the buyer was reached but never trusted. The return-to-sender rate is the balance sheet made physical — the exact fraction of your demand that was rented rather than owned. Every citation and every agent call has the same structure: it gets you in front of someone, and what you do with that moment decides whether it deposits into the owned ledger or evaporates.

So the discipline is: rent discovery on all three surfaces, and route every bit of it toward something you own — a captured relationship, a repeat buyer, a reputation people seek out by name. That owned end of the spectrum is the only thing no platform can reprice.

The audit: score your citability, callability, and owned trust#

Run this on your own business this quarter. It is three dimensions, each with a concrete check and a thing to measure. Score each row honestly as strong, weak, or absent.

Dimension	Check	How to test it	What to measure
Citability	Do answer engines quote and name you?	Ask the major assistants the 15–20 questions your buyers actually ask. Note whether you appear, are named, and are quoted accurately.	Share of target questions where you are cited; accuracy of the quoted claim; referral traffic from assistant domains.
Entity clarity	Is your one-line description consistent across the web?	Search your company name across independent surfaces and compare the descriptions.	Number of distinct one-liners in circulation (fewer is better); whether the assistants describe you the way you describe yourself.
Passage craft	Do your key pages lead with the claim and survive decontextualization?	Pull five sections at random, read each with no surrounding context, and ask if it still states a specific, attributable claim.	Fraction of sampled passages that stand alone; presence of specific numbers, dates, defined terms.
Callability	Is your core capability exposed as a callable tool?	Can an agent invoke your key verb without a human in the loop? Is there a name, description, and typed schema?	Yes/no on exposure; whether the description states when-to-use, when-not-to, and return shape.
Description quality	Would a planner select you over a rival?	Put your tool description next to a competitor's and ask a model which fits a given task.	Selection rate in that test; presence of an enum-typed schema and informative errors.
Registry presence	Are you where agents look?	List the registries and default connector sets in your domain; check if you are in them.	Count of relevant registries you are listed in; observed reliability of your endpoint.
Owned trust	Does discovery convert to something you keep?	For each channel, ask: can a third party cut this connection? Then trace what fraction of reached people become owned relationships.	Cost per captured relationship; 90-day retention; in COD, delivery-acceptance and reorder rate.

Two honest cautions about measurement. First, all of column-two and column-three feedback is a distribution you sample, not a scoreboard you read — answers are non-deterministic and rank trackers for this surface do not really exist yet, so re-test on a schedule rather than trusting a single check. Second, weight the owned-trust row highest. A perfect citability and callability score built on zero owned conversion is a business renting a crowd for an afternoon.

Start here Monday#

Do these seven things this week. They are ordered so the cheapest, highest-leverage moves come first.

1. Run the citation test. Ask the major assistants your 20 buyer questions and write down where you appear, get named, or get misquoted. This is your baseline; it takes an hour.
2. Fix your entity. Pick one canonical one-line description and make it identical everywhere you control — site, profiles, docs. Consistency is the cheapest citability lever.
3. Rewrite your three most important pages passage-first. Lead each section with the claim, make each section self-contained, and replace every vague quantity with a specific, sourced one.
4. Expose one capability as a callable tool. Choose the single verb an agent would most want, and write its name, description (with when-to-use and when-NOT-to-use), and a typed, minimal schema. One tool, done well, beats ten stubs.
5. Pressure-test the description. Put it beside a competitor's in a model and ask which fits a task. Rewrite until you win the selection.
6. List where agents look. Find the registries and default connector sets in your domain and get in the ones that exist, early.
7. Attach a costly signal and a capture point. For every one of the above, decide what durable thing a reached person converts into — a list, an account, a first transaction — and what expensive-to-fake signal earns it.

The surface is early and moving, and the specific tactics will churn under you. The direction will not: discovery is fracturing from ranking-on-a-page into being cited by an answer and called by an agent, and the only spend with a future in it is the trust you own at the end of the chain. Build for all three columns, and route every one of them toward the one you keep. Start with the citation test today; it is an hour, and it will tell you exactly how invisible you currently are.