Walk into a UAMS hospital for the first time and you will notice something before you notice anything else: the signs. Colored lines on the floor that lead you from the main entrance to Radiology. Overhead signage at every corridor intersection. Information desks staffed by people whose job is to get you where you need to go. This is wayfinding, and it is not decoration. It is infrastructure.
In 2025, UAMS Web Services began building the same kind of infrastructure for a new category of visitor: the AI agents that increasingly arrive at our websites on behalf of the humans who used to visit them directly.
The visitor profile has changed
A decade ago, most people found UAMS content through a search engine. They typed a query, scanned a results page, clicked a link, and read the page themselves. The humans did the wayfinding.
That pattern is breaking down. A growing share of the people looking for UAMS content never visit a UAMS page directly. They ask an AI assistant. They type their question into ChatGPT, Claude, Gemini, or Perplexity and read the synthesized answer. Whether the answer is accurate depends on whether the AI assistant understood what it was reading.
For a university, research enterprise, and academic medical center with more than 200 tracked web properties spanning clinical specialties, colleges, institutes, and operational units, that is not a small question. An AI agent arriving at uams.edu without orientation has to infer where to find provider information, which subdomain handles degree programs, what is a college versus an institute, and which news source is authoritative. Every one of those inferences is a place where a hallucination can start.
The gap we set out to close
The UAMS web ecosystem has a well-established architectural model. A primary gateway at www.uams.edu routes traffic. A technical origin at web.uams.edu hosts root assets. A brand authority at communications.uams.edu centralizes web governance. Specialized clinical, academic, and institutional subdomains each have a defined scope. Humans who know the ecosystem can navigate it effectively.
But the ecosystem was never designed to be read by machines. The relationships between domains were documented for staff, not for agents. The authority of each subdomain was implicit in its URL, not declared in a file that an LLM could parse in seconds. When a human visitor had to ask a question, they asked a person. When an AI agent had to answer a question, it was on its own.
UAMS AI Wayfinding is the initiative that sets out to close that gap.
What we are building
Wayfinding is a standardized, machine-readable layer that sits on top of the existing web architecture. As the initiative rolls out across the ecosystem, each pillar and its subsites will publish a coordinated set of three files:
- robots.txt defines crawl permissions for AI agents, with explicit rules for GPTBot, ClaudeBot, Google-Extended, and others.
- llms.txt is the handshake. A short, human-readable summary of what the domain is and what it is authoritative for, written specifically for consumption by LLMs.
- llms-full.txt is the directory map. A deeper index with behavioral directives, content inventories, and cross-links to sibling domains.
Together these files do for AI agents what floor lines and overhead signage do for hospital visitors. They answer the questions that would otherwise be inferred, poorly.
Why this is different from SEO
Wayfinding is not search engine optimization, although the two share some surface features. SEO is a decades-old discipline focused on helping a page rank in a list of results. Wayfinding is about helping an agent understand a domain well enough to cite it correctly inside a generated answer.
The difference matters because the failure modes are different. A poorly optimized page fails by ranking lower. A poorly understood domain fails by being misrepresented in front of a patient, student, or referring physician. When a generative AI tool tells someone that UAMS offers a program it does not offer, or lists a specialty at the wrong institute, the cost is not a lost click. It is a lost opportunity to be trusted.
The hospital metaphor, all the way down
The wayfinding metaphor is not a stretch. Every UAMS employee already understands wayfinding as a concept. Everyone has given directions in the Central Building. Everyone has pointed a lost visitor toward the right elevator. Extending that same idea to the digital ecosystem is a framing we expect to land with deans, directors, and developers alike as the initiative is introduced more broadly.
The metaphor also holds up at a deeper level. Hospital wayfinding is designed around the fact that visitors arrive without context and need to orient quickly. AI agents have exactly the same problem. They do not have years of institutional memory. They do not know that the Winthrop P. Rockefeller Cancer Institute is the oncology authority or that the Translational Research Institute is where clinical trial listings live. They need signs.
The scale problem, stated plainly
A federation of over 200 tracked web properties is not a federation that can be governed by hand. Each property has its own scope, its own content editors, and its own relationships with sibling properties. Without a standardized machine-readable layer, each one also has its own AI-readability problem.
Wayfinding solves this at the architecture level rather than the page level. One file triad per subsite. One governance model across the federation. One cross-link pattern that scales without becoming a maintenance burden. When a new subdomain launches, the Wayfinding layer is part of the launch checklist, not an afterthought.
What is next
Wayfinding at UAMS is a living initiative, not a finished one. As new subdomains launch, the file triad is now part of the production-readiness checklist. As AI tools evolve, the directives inside llms-full.txt will evolve with them. Communication and training for departmental stakeholders across the institution is part of the work still ahead.
What makes the work sustainable is that it is built on the same foundation as the rest of the UAMS web ecosystem: centralized governance through Web Services, standardized files stored in a shared internal repository, and a common serving mechanism across every website in the federation. The established Virtual Root architecture provided the stable substrate. Wayfinding is what it now supports.
For institutions in higher education and healthcare that are starting to ask how their digital presence will fare in an AI-first discovery environment, the lesson from our rollout is short. The agents are already visiting. The question is whether your site has signs.
The UAMS Web Services team maintains the Wayfinding file triad across all pillar domains in the ecosystem. Use the form below for questions or to report citation errors from AI tools.