Your documentation has a new reader, and it doesn’t use the nav bar.
Instead, AI coding agents are becoming a primary “reader” for the docs — and, maybe before too long, the single-largest reader. Across Mintlify-powered documentation sites, AI coding agents now account for roughly 45% of all requests — nearly tied with human browser traffic. Claude Code alone generates more requests than Chrome on Windows.
Docs aren’t geared toward a single audience anymore. Instead, we are seeing the initial tsunami of agentic readers. And we must adapt to that new reality to model our docs in an optimal way for both readers. A structure that looks clean to a person can still be misleading for a machine trying to pull context from it.
More importantly, docs used to be something people read after they’d decided to buy. Now they’re part of the integration path an agent relies on during technical evaluation before sales gets involved. If an agent is misreading docs (because we haven’t accounted for agentic readers), it could have catastrophic consequences for the business.
The old rules aren’t necessarily wrong. Reader intent and the separation among tutorial, how-to, reference, and conceptual content remain the right foundation. But they do need a significant update to account for the wave of agentic scraping and ingestion, both today and in a future powered entirely by agentic workflows.
That changes the stakes. Bad information architecture (IA) used to mean a confusing page. Now it can mean a wrong integration. If an assistant recommends a deprecated endpoint, misses a dependency, or builds against stale guidance, that’s not a cosmetic failure. It can delay an evaluation, erode trust before it’s built, or quietly cost an adoption nobody traces back to a paragraph in an install guide. That makes it as much a product problem as a documentation one.
The new reality of agentic readers
AI agents often reach a page in one of three ways: a direct fetch, a crawl through raw HTML, or a targeted query through an MCP server.
And that leads to new points of failure. Imagine a deprecated v1 endpoint appearing earlier on a page than its v2 replacement — the natural order for a person reading top to bottom, with the oldest first. A human understands that as history. An agent pulling an isolated slice of that page has no way to know the order means “this came first,” not “this is current.” It just picks whichever one it finds.
Build With Fern has shown that API reference pages can be an order of magnitude larger in rendered HTML than in clean Markdown. Most of that gap is navigation and styling — overhead an agent must wade through before reaching what matters.
Mintlify, meanwhile, reports that one engineering team compared raw code, improved docs, and structured docs connected through MCP on their monorepos. The structured version produced 64% more precise answers, 39% better discoverability, half as much token usage, and completed tasks 1.5x faster.
How agentic parsing can spiral
Picture a common setup in any hardware-plus-software stack: a software layer that depends on a minimum firmware version of the hardware it runs on. If that requirement is tucked six paragraphs into an install guide, it’s fine for a person skimming the page.
That same information is fragile for an agent that only pulled a narrow slice of the install page to preserve token consumption and perform more efficiently.
That information is already in the docs, just presented in a format that assumes the human brain’s context window and throughput. An agentic-first structured version—some kind of fact it can query, rather than a sentence it misses- is better.
This software version requires that firmware version or higher — a fact the system can query, not a sentence it might miss.
Most teams that ship hardware and software together have exactly this kind of cross-dependency somewhere in the installation flow. It’s an information architecture problem before it’s a publishing-format problem, and it usually stays invisible until something downstream breaks.
Why an llms.txt file isn’t enough
An llms.txt file is, at best, a flat list of pages that helps direct agents. But it can’t provide all the extra nuance needed for agents crawling the website — such as identifying what was deprecated or replaced, or any necessary dependencies that tie to different versions (such as earlier ones) of a product.
The better pattern is a single source of truth that renders information in multiple formats. A human-facing site keeps its navigation, examples, and narrative flow, while the agent-facing layer can be flatter and denser, ordered by dependencies rather than by page design. Both present the same information—just for a human or a robot, depending on who’s visiting.
Some platforms support marking content as AI-only or human-only at the component level, so a single source file renders differently depending on who’s reading it. The architecture for all this already exists with frontmatter, Docusaurus, Sphinx, or whatever platform you’re already running.
Why this matters for documentation teams
Some companies are already treating this as an infrastructure problem, not a formatting one. That documentation must be useful in context for the systems people now rely on to build products, and not just an SEO-first format.
For example, Groq’s docs include language aimed directly at agents, along with llms.txt and llms-full.txt files that make the same documentation easier for models and AI tools to consume.
The broader pattern matters more than any one implementation. Once documentation is part of the integration path, it must be structured for both people and machines.
The underlying IA problem hasn’t changed: make information findable for whoever is looking for it. That just has to account for the tsunami of robots that are now reading these websites.
