Building for the Agentic Web: What Your Site Needs When AI Is the User
When the primary user of your website is an AI agent rather than a human, different design principles apply. Most sites are not ready.
Web design has always been a discipline defined by its primary audience. When the audience was humans, design optimised for reading speed, visual hierarchy, and emotional engagement. When the audience began to include search engine crawlers, design adapted to include structured metadata, canonical tags, and internal link architecture. We are now at the beginning of a third transition: a growing proportion of meaningful interactions with websites is being conducted by AI agents rather than human users or traditional crawlers. These agents have requirements that neither human-centred design nor traditional SEO addresses.
The Seven Requirements of Agent-Ready Content
- 1Machine-parseable structure: every key fact is expressed in structured data, not only in visual design or prose
- 2Entity disambiguation: your primary entity is uniquely identifiable, with sameAs links that allow agents to cross-reference external records
- 3Consistent attribute expression: entity attributes (name, location, founding date, description) are identical across schema, body text, and metadata
- 4Programmatic discovery: a /.well-known/llms.txt or ai.txt file summarising your content and entity for agent pre-screening
- 5Rate limit transparency: your robots.txt includes crawl-delay or agent-specific rate limits that agents can respect
- 6Direct answer formatting: key facts are expressed as direct answers to anticipated queries, not embedded in comparative or narrative prose
- 7External validation: at least one sameAs link per primary entity resolves to an authoritative external source that confirms the entity's existence
Schema Markup as the Agent Interface
For AI agents, schema markup is not a search engine optimisation tactic, it is the primary interface between the agent and the site's content. A well-structured Organisation schema tells an agent the entity's canonical name, its founding date, its legal jurisdiction, its primary activities, and the external sources that validate its existence. An agent retrieving this information from schema does not need to parse and interpret prose; it reads the structured data directly. Every piece of information that an agent might need to trust and use your content should be expressed in schema markup, not only in body text.
●The MCP (Model Context Protocol) standard, developed by Anthropic and adopted by major AI providers, defines how AI agents interact with external resources programmatically. Sites that implement MCP-compatible endpoints will be accessible to Claude-based agents by default, creating a direct channel for AI-mediated access that bypasses traditional web crawling.
Performance Requirements for Agent Access
AI agents typically access dozens or hundreds of pages per session. Response time requirements are more stringent than for human users — agents have session timeouts and rate budgets that determine how many pages they can process. A page that takes 3 seconds to load loses a significant portion of an agent's available budget, reducing the probability that it processes the full content. Core Web Vitals improvements that reduce Time to First Byte and Largest Contentful Paint directly improve agent accessibility.
Internal Linking as Agent Navigation
Human users navigate by clicking links they find visually compelling. AI agents navigate by following links with the highest semantic relevance to their current task. This means anchor text quality is critical for agent navigation: agents follow links whose anchor text describes content relevant to their task and ignore links with generic anchor text like "click here", "read more", or "learn more". Every internal link should describe the content it points to in specific, entity-rich language. This doubles as excellent SEO practice and is directly aligned with agent navigation requirements.