Speaking Your AI Agent’s Language

How to Structure Website Content, Feeds, and Data for Discovery in LLM-Powered Systems
By Robert Derow, Justin Kuron, and Matt Woodhull
Blog Post

Websites have always been structured for one primary consumer: humans using browsers.

Pages were designed to rank. Layouts were optimized to convert. Metadata was engineered to climb SERPs. The web’s architecture was built around a simple assumption: people would navigate it page by page.

That assumption is starting to break.

Large language models (LLMs) don’t browse your site. They ingest, summarize, and retrieve fragments of it. Increasingly, they act on that information through agents that discover, evaluate, and recommend products or actions to users, or even to other agents.

If your content is still designed purely as a collection of human-readable pages, you’re already behind.

“We’re quickly approaching a tipping point where more agents than humans will visit your site. This fundamentally changes the question: Who is your website actually for?” — Chris Andrew, CEO, Scrunch

From Browsing to Retrieval

The web was built around pages. AI systems operate through retrieval.

We’re now seeing a structural transition from:

Zero-click searches have continued their steep climb , growing 13 percentage points — from 56% to 69% — in just one year, driven by the accelerating rollout of AI Overviews. Within AI-native interfaces, the effect compounds further: around 93% of Google's AI Mode searches end without a click.

As for AI Overview coverage, a twelve-month BrightEdge analysis through February 2026 found AI Overviews now trigger on nearly half of all tracked queries — a 58% increase year over year — with some industries like education (83%) and B2B tech (82%) seeing the heaviest exposure. Meanwhile, ChatGPT's ascent as a primary discovery channel continues: their 5.5 billion visits in January 2026 ranks them as the fifth most visited website globally — ahead of Reddit, Wikipedia, and X — having first broken into the top 15 just two years ago.

Consumers no longer navigate from link to link. Instead, they ask systems to synthesize the web for them—and those systems do not “experience” your website the way a person does.

Experiences Are for Humans, Data Is for Machines

Humans don’t want pages—they want experiences. They want to be informed, entertained, reassured, and inspired. They want intuitive navigation, compelling storytelling, brand expression, and effortless journeys.

That is what great digital design delivers—and it remains essential.

But machines want something different: data.

Not vibes, layout hierarchy, or scroll-triggered animation. And not the layers of JavaScript orchestrating a dynamic interface. They want structured, explicit, machine-readable information.

An LLM doesn’t care about:

It cares about:

In many websites, the ratio of meaningful content to supporting JavaScript and client-side logic is wildly imbalanced. When a typical consumer page is rendered for bot consumption, the majority of tokens may consist of scripts, styling logic, and dynamic components—not the actual information the model needs.

By contrast, an AXP-style (agent experience optimized) or feed-based representation of the same content can be two orders of magnitude lighter in tokens while delivering significantly higher information density.

Why does this matter?

Because large language models operate within token constraints. The higher the signal-to-token ratio, the more of your useful information a system can ingest, reason over, and return to the user behind the prompt.

In practice, improving retrievability means simplifying how information is delivered: less superfluous JavaScript, fewer nested components, more resolved HTML, and more structured exports.

The cleaner your data surface, the more accessible you are to AI systems.

This is why feed-based architectures—once considered back-office plumbing—are quietly becoming strategic assets.

Consider the systems already operating this way:

These systems don’t experience your site. They consume your data.

Increasingly, they don’t just crawl periodically. They subscribe. And these subscription-based systems reward clarity, consistency, schema discipline, version control, and deterministic fields—not clever copywriting.

Designing for the Personal Agent Era

The implications grow even more significant as personal agents begin to enter the picture.

Soon, it won’t be only search engines or retail platforms accessing your data. Personal agents will retrieve it as well, acting on behalf of individuals.

For example, a consumer might ask their personal shopping agent to find a product. The agent then visits your digital surface, retrieves structured product data, and compares availability, pricing, constraints, and delivery timelines across multiple sources. It validates return policies and warranty terms, then executes a transaction through an API.

At no point does that agent need to navigate your UX.

What it needs is the ability to retrieve accurate information, validate guarantees, and complete a purchase reliably.

Content locked inside experiential layers designed purely for humans creates friction for agents. Structured, explicit, and easily accessible data lets agents complete tasks efficiently.

The takeaway isn’t that experiences matter less. It’s that organizations now need to design for two consumers simultaneously:

  1. Humans, who want experiences
  2. Machines (and agents), which need structured truth

Organizations that cleanly separate these layers—experience on top, structured data beneath—will outperform those that continue treating their website as a visual document rather than a data system.

In an AI-mediated world, accessibility is not just about usability. It’s about retrievability.

Render the Web the Way Bots See It

Here’s a hard truth: what humans see is not what bots see.

Modern websites are often:

To a human, this feels engaging and intuitive. To an LLM or agentic system, it can appear incomplete or ambiguous. A page that appears rich and interactive in the browser may look very different when accessed by a model or retrieval system.

Fortunately, improving machine visibility doesn’t require radical changes. The emerging best practices are straightforward, though often overlooked:

  1. Stop blocking the systems you want visibility in
    Many organizations still restrict bots in robots.txt because of legacy SEO policies, analytics noise, or security defaults. In some cases, entire sections of a site become unintentionally unreadable to AI systems. Before optimizing anything else, confirm that your core pages, feeds, and endpoints are actually accessible to reputable AI crawlers and retrieval systems.
  2. Ensure server-side or static rendering for core content
    Critical product, pricing, and availability information should exist in fully resolved HTML. If essential information only appears after client-side hydration, it may not exist to the model.
  3. Avoid hiding material information behind user interactions
    Accordion tabs, modal windows, or login gates can suppress retrievability and make information harder for agents to access.
  4. Keep content stable across renders
    Agents value determinism over creativity. If the same URL produces materially different structures across sessions, trust declines and retrieval becomes less reliable.

If your content doesn’t exist in a clean, fully rendered state, it doesn’t become invisible to an LLM. But it does become harder to parse, easier to misinterpret, and less likely to be retrieved accurately.

Think in Entities, Not Pages

LLMs don’t interpret the web through URLs. They interpret it through entities and relationships.

Instead of asking, “How do we optimize this product page?” ask, “How should this product entity be defined?”

A product is not a page. It’s an object with:

This is where structured data, APIs, and feeds begin to outperform traditional SEO tactics.

A useful mental model for this shift: think of your website not as a document, but as a database.

Brand narrative, differentiation, and storytelling still matter for humans. But the underlying database layer must be explicit, consistent, and machine-readable.

When information is modeled as entities rather than pages, it becomes far easier for AI systems to retrieve, validate, compare, and act on.

Feeds: The Underrated Driver of LLM Visibility

Merchant feeds are not new—but they are becoming newly important.

Feeds perform well in LLM environments for a simple reason: they deliver information in a format that machines can reliably interpret. Several characteristics explain why:

A single well-designed product feed can outperform hundreds of beautifully written pages when it comes to model comprehension.

You can already see this dynamic in retail AI systems like Amazon Rufus and Walmart Sparky. Their conversational shopping interfaces rely on structured catalog data rather than traditional page crawling. Retailers are increasingly monetizing inside these AI-driven experiences, and the infrastructure underneath them is feed-driven, not page-driven.

And retail is just the first visible example.

As LLMs become embedded across search, productivity tools, operating systems, and personal agents, feeds and API-based access will likely extend far beyond commerce. Structured data pipelines—not web pages—will become the primary integration layer powering these interactions.

In other words, feeds are becoming the handshake between brands and AI systems.

Whether the data involves product inventory, pricing, service availability, financial terms, policy details, or real-time capacity, LLM-powered systems will increasingly rely on structured endpoints and subscription-based data access to retrieve trusted information at scale.

Similarly, as assistant-native AI becomes commercialized, structured data will shape how products, services, and recommendations appear inside synthesized answers—not only in retail, but across travel, financial services, healthcare, education, and B2B decision workflows.

If your SKU data—or any core entity data—is inconsistent, incomplete, poorly versioned, or inaccessible via structured interfaces, you risk becoming invisible. Or worse, misrepresented.

Preparing for Agent-to-Agent Communication

We are moving toward a world where transactions are no longer linear—and no longer human-orchestrated.

Instead of a person navigating multiple tabs and comparing options, the workflow may look like this:

Projects like OpenClaw , an open framework that enables agents to browse, reason, and transact across the web—illustrate how quickly agent-driven execution is moving from passive retrieval to active task completion.

In this environment, agents don’t want prose. They want answers with guarantees.

This requires:

If a human reads your site and infers meaning, that’s acceptable—and often intentional. If an agent has to infer meaning, the structure has already failed.

Ambiguity in agentic systems is expensive. It introduces friction, increases latency, reduces trust scores, and increases the likelihood of inaccurate responses—or of being excluded from automated decisions altogether.

But clean data alone won’t be enough.

The Rise of AI Protocols

We are still in the early days of agent-to-agent standards. Different model ecosystems—Google, OpenAI, Anthropic, Perplexity, and others—are each developing their own integration patterns and retrieval frameworks.

Over time, these standards will become more formalized and more sophisticated.

Brands will not just need structured data. They will need infrastructure capable of coordinating how their systems—websites, feeds, and APIs—communicate across multiple AI protocols simultaneously.

Just as brands needed a content management system (CMS) in the web era, they will need a standards layer in the agent era. Rebuilding your architecture every time Google, OpenAI, or another model introduces a new protocol simply won’t scale.

In the web era, brands needed:

In the agent era, they will need:

What will matter most is not how eloquently information is written, but how precisely it is defined and how reliably it can interoperate across systems.

What Not to Do: The AEO Markdown Trap

One of the most common mistakes we’re seeing right now is creating separate “LLM-optimized” pages.

These often take the form of:

The intention is understandable. Teams want to “optimize for answer engines.”

But this approach often backfires. Here’s why:

SEO and AEO must work together. Creating parallel content architectures undermines both.

The leaders ahead of the curve are taking a different approach. Rather than creating separate “AI pages,” they maintain a single canonical source of content that can be rendered differently for humans and agents.

Not two websites.
Not two truths.
Not a hidden directory for bots.

One source of truth, rendered differently for each consumer: experience-rich for humans, structured and machine-readable for agents.

LLMs don’t need special pages. They need:

If your human-facing content and machine-facing content diverge, models may struggle to trust either. But when both originate from the same canonical system—rendered appropriately for each audience—you preserve authority, improve retrievability, and avoid building technical debt into the next era of discovery.

The Real Goal: One Source of Truth, Many Consumers

The future-proof architecture is conceptually simple, even if operationally demanding: One content model. One source of truth. Multiple outputs.

From that unified model, organizations can generate:

This does not mean UX and UI matter less.

Humans will always need thoughtful design, intuitive navigation, emotional storytelling, and brand expression. The experiential layer remains critical for trust, differentiation, and conversion.

But beneath that experience layer, new structures are emerging—ones designed for agents, bots, and AI systems:

When organizations serve humans and machines simultaneously:

The key is separation of concerns. Experience lives in the presentation layer, while structure lives in the data layer. And both originate from the same canonical system.

This is not an SEO initiative or a feed optimization project. It’s a redesign of the content operating system—one that acknowledges a website is no longer just a destination for people, but an interface for machines.

And the brands that architect for both will outperform those still designing for only one.

The New Rules of Discovery

This shift isn’t theoretical. Discovery is becoming synthesized.

When answers are assembled by models rather than browsed through links, brands are no longer competing for clicks. They’re competing for inclusion, which operates under a different set of rules.

In a synthesized environment, the system becomes accountable for what it selects—and it increasingly favors sources that are:

This is not about ranking signals. It’s about eligibility.

If your content is ambiguous, inconsistently structured, inaccessible via robots.txt, fragmented across duplicate versions, or buried behind heavy client-side logic, you may not be excluded outright—but you become less reliable. And in AI systems, reliability is a gating function.

Inclusion is governed by more than keyword density. It depends on:

As discovery becomes synthesized, accountability shifts upstream. Models must justify their selections, and agents must validate their actions.

The most successful brands will not be those that optimize hardest for ranking. They will be those that make themselves easiest to understand, validate, and act on.

A Practical Blueprint for Marketing and Digital Leaders

Understanding this shift is one thing. Operationalizing it is another.

To begin translating these ideas into action, leadership teams should focus on six priorities:

1. Audit your machine-readable surface area
Start by taking inventory of how accessible your data actually is to AI systems. This includes:

Ask a simple question: “If a model had to understand our entire catalog without reading our brand copy, could it?”

2. Move from page templates to entity models
Redesign your CMS and content architecture around entities rather than pages:

Each entity should have explicit attributes, defined relationships, and deterministic fields—not just sections of formatted copy.

3. Strengthen feed and API governance
Feeds and endpoints should be:

Inconsistent feeds are silent visibility killers. In an agent ecosystem, stale price data, conflicting availability, or inconsistent policy language doesn’t just hurt ranking—it erodes machine trust.

4. Make loyalty machine-readable

In a human journey, loyalty benefits often surface late—at checkout or after login. In an agent-mediated journey, those benefits need to be visible much earlier.

If a personal shopping agent is comparing retailers, it will evaluate more than:

It will also consider:

If those data points are not structured and accessible, the agent cannot factor them into its optimization logic. And if they’re not factored in, they won’t influence the decision.

In the agent era, loyalty programs must evolve from marketing overlays into structured economic signals.

5. Eliminate duplicate truth systems

Collapse separate SEO, AEO, and feed teams into a unified content governance structure.

AI collapses silos. Your organization should too.

6. Prepare for agentic transactions

Even if agentic commerce feels early in your category, begin preparing:

Agent ecosystems will reward brands that reduce decision friction—not only for consumers, but for the systems acting on their behalf.

From Visibility to Selection

For two decades, digital strategy focused on visibility. The next decade will focus on selection.

Being visible in an AI-generated answer isn’t enough. The real advantage comes from being structured and accessible in a way that makes selection easy.

If your content is blocked, inconsistently rendered, structurally ambiguous, or difficult to parse, it doesn’t get debated—it gets bypassed.

The brands that win will not be those with the flashiest pages. They will be those with:

In a world where AI systems mediate discovery, the question is no longer

“Can we rank?”

The real question is:

“Can we be accessed, understood, retrieved, recommended, and acted upon?”

The organizations that redesign now won’t just appear in answers. They will shape the decisions those answers drive.