AI Search Optimisation 2026: AEO, GEO & Agentic SEO 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 |
AI search optimisation in 2026 is the unified practice of ensuring brand visibility across all AI-powered search interfaces - Google AI Overviews, Perplexity, ChatGPT, Microsoft Copilot, and autonomous agents. It encompasses AEO (Answer Engine Optimisation), GEO (Generative Engine Optimisation), agentic SEO, and LLM optimisation. The core strategy is building citation-worthy content with comprehensive structured data, entity consistency, and machine-readable infrastructure.
Optimise for AI search engines by implementing five key strategies: (1) answer-first content architecture - lead every page with a clear, quotable answer, (2) comprehensive structured data - layered JSON-LD including Person, Organisation, Article, and FAQ schema, (3) entity consistency - use canonical terminology across all content, (4) AI crawler access - allow AI crawlers in robots.txt and deploy llms.txt, and (5) topic clustering - build interconnected content that establishes topical authori
AEO (Answer Engine Optimisation) optimises for citation in AI-generated answers. GEO (Generative Engine Optimisation) optimises for LLM adoption of your terminology and frameworks. Agentic SEO optimises for discovery by autonomous AI agents. All three share common foundations - structured data, entity consistency, and content authority - but target different AI systems and outcomes. In 2026, they are converging into a unified AI search optimisation discipline.
Measure AI search visibility through three metrics: citation frequency (how often AI answer engines cite your content for target queries), citation share (your proportion of citations compared to competitors), and entity accuracy (how accurately AI systems represent your brand). Test 20-50 target queries monthly across Perplexity, ChatGPT, Google AI Overviews, and Copilot. Track these metrics alongside traditional SEO metrics for a complete visibility picture.
AI search optimisation requires: server-side rendering of critical content (H1, meta tags, structured data), comprehensive JSON-LD structured data on every page, llms.txt deployed at three paths (/llms.txt, /.well-known/llms.txt, robots.txt directive), AI crawler access in robots.txt, semantic HTML throughout the site, and fast page load times (under 2 seconds for critical content). These technical foundations ensure AI systems can discover, parse, and cite your content.
Build topic clusters with pillar-and-spoke architecture. AI systems evaluate authority at the topic level. A single page on 'agentic commerce' is less authoritative than a cluster of interconnected pages covering