Designing Websites for AI Agents: How to Factor in Non-Human Users

While humans visit websites, they rely largely on spatial layout, hierarchy, and other visual elements like typography. Aesthetics are much less important to AI agents, which are focused on raw data streams and the document object model, or DOM.

Design principles for AI, sometimes called “agent experience” (AX), moves away from the traditional rules. This explainer goes over how any experienced web designer in Sydney and beyond can create agent-friendly websites.

Designing for AI agents vs humans

Essentials for AI Agent-Friendly Websites

Creating a workable site for non-human users comes down to three golden rules. First, remember that they favour interpretability over aesthetics. Even a gorgeously modern and sleek layout can be completely opaque AX-wise. The key is never to convey meaning or state only via colours, CSS, or layout positioning. Use explicit code signals agents can understand, or structured schema markup.

Second, remember that LLM agents are non-deterministic users. This means they need to be guided via explicit, self-describing, and predictable interface elements to prevent hallucinations or guessing. If a website relies on client-side dynamic states or UI cues, the agent often gets stuck guessing what to do next. Use machine-readable contracts with explicitly defined validation rules, state attributes, and input formats.

Perhaps most importantly, don’t neglect context engineering as it pertains to token efficiency. Agents pay for data they ingest via “tokens,” as well as context window capacity. When they’re forced to parse 2MB pages weighed down with deep DOM nesting, redundant visual markup, and bloated Javascript, performance degrades and failure rates increase.

Best Practices and The Dual-Interface Future

Non-negotiables aside, there are other ways to optimise for AX fundamentals. Providing a lightweight, markdown-formatted map of a site’s core offerings, APIs, and key pages is arguably the most notable one. This can be done by positioning llms.txt at the root of the website.

• Put ARIA and semantic HTML to work in buttons, form inputs, and other important elements. Explicit name, id, and aria-label attributes, along with clear use of <main>, <header>, and <nav> make it easier for agents to filter out site noise.
• Recognise raw copy as inefficient for AX, and use rich structured data to avoid forcing agents to extract actionable details (e.g. ratings, price) from it. Instead, embed JSON-LD scripts directly into the document head.
• Account for agents making mistakes by designing safely reversible action sequences. This can be done using structured, machine-parsable error hints with resolution paths, or by offering preview states and dry run endpoints (e.g. POST /api/cart/checkout?mode=validate).

The internet of the future will embrace a hybrid web architecture accommodating both the human layer (GUI) and its agentic counterpart (structured API, CUI). Forward-minded engineering teams can prevent loss of discovery — and subsequently, transactional volume — by adapting ahead.