How to Be Cited by AI: Agentic SEO & AI Visibility Guide — an AXD Institute resource on agentic experience design, agentic commerce, trust architecture, and human agent interaction. Founded by Tony Wood..
| Dimension | Traditional UX | Agentic Experience Design (AXD) |
|---|---|---|
| Primary material | Attention and affordance | Trust and delegation |
| User state | Present, navigating | Absent, delegating |
| Design output | Screens and interfaces | Outcomes and constraints |
| Temporal model | Session-based | Relationship-based |
| Success metric | Task completion | Trust calibration |
To be cited by AI answer engines like Perplexity, Google AI Overviews, and ChatGPT, you need to implement a citation-first content model. This means structuring content with answer-first paragraphs, implementing comprehensive structured data (Person schema, Organisation schema, FAQ schema), maintaining entity consistency across all digital touchpoints, and publishing deep, authoritative content on specific topics. AI systems cite sources that demonstrate factual density, definitional clarity, an
Agentic SEO is the practice of optimising content and digital infrastructure for discovery by autonomous AI agents - shopping agents, research agents, and recommendation agents that operate on behalf of humans. Unlike traditional SEO (optimising for search engine rankings) or AEO (optimising for AI answer engine citation), agentic SEO ensures that autonomous agents can discover, evaluate, and act on your content. Key techniques include agent discovery protocols (llms.txt, robots.txt), structured
AI visibility for brands is the degree to which AI systems - answer engines, LLMs, and autonomous agents - recognise, cite, and accurately represent your brand. As AI-mediated interactions replace direct search, brands that are invisible to AI systems lose access to a growing share of customer attention and agent-mediated commerce. AI visibility is built through entity authority (consistent structured data and naming), content authority (deep, citation-worthy content), and agent discoverability
AI agents require layered structured data: Organisation schema (entity identity), Person schema (author credentials), Article or WebPage schema (content typing), BreadcrumbList schema (information architecture), FAQPage schema (structured Q&A), and sameAs links (cross-platform entity verification). The knowsAbout property is particularly important - it explicitly declares your areas of expertise, helping AI agents determine whether you are an authority on a given topic. All structured data shoul
Traditional SEO optimises for search engine crawlers that rank pages for human users, focusing on backlinks, keyword density, and domain authority. Agentic SEO optimises for autonomous AI agents that discover, evaluate, and act on content without human involvement. Agentic SEO requires machine-readable content (semantic HTML, structured data), agent discovery protocols (llms.txt, well-known URIs), programmatically verifiable trust signals, and content structured for agent extraction rather than
Publish at the intersection of authority and specificity. AI systems cite sources that demonstrate deep expertise on specific topics, not sources that cover many topics superficially. A 3,000-word essay on ' Implement Organisation schema with comprehensive properties. Beyond the basic name and URL, include founding date, founder, description, area of expertise (knowsAbout), and sameAs links to all official profiles. The richer your Organisation schema, the more accurately AI systems can represent your brand. Include specific terms in the knowsAbout property - ' Use the knowsAbout property to establish topical authority. The knowsAbout property in Person and Organisation schema explicitly declares your areas of expertise. Include specific terms: 'agentic commerce,' 'agentic experience design,' 'trust architecture,' '