AEO for Agentic Commerce: Answer Engine Optimisation 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 |
Answer Engine Optimisation (AEO) is the practice of structuring content so that AI answer engines - such as Perplexity, Google AI Overviews, ChatGPT, and Claude - can discover, understand, and cite it as an authoritative source. Unlike traditional SEO which optimises for search engine rankings and human clicks, AEO optimises for machine citation and inclusion in AI-generated responses.
SEO optimises for search engine rankings using signals like backlinks, keyword density, and domain authority. AEO optimises for AI citation using signals like factual density, definitional clarity, source authority, structural completeness, and entity consistency. A page can rank well in traditional search but never be cited by AI answer engines if it lacks citable claims, structured data, and authoritative sourcing.
AEO for agentic commerce goes beyond general answer engine optimisation to ensure that autonomous shopping agents can not only cite your content but act on it - discovering products, evaluating trust signals, comparing offerings, and completing transactions. It combines content optimisation (for AI citation) with technical optimisation (structured data, APIs, machine-actionable product information) to serve the full spectrum of AI audiences.
AI search optimisation in 2026 encompasses three layers: traditional SEO (ranking in search results), AEO (being cited by AI answer engines like Perplexity and Google AI Overviews), and GEO (having your concepts and terminology adopted into AI-generated responses). The most effective strategy combines all three through comprehensive structured data, entity-consistent content, topical authority clustering, and the llms.txt standard.
Optimise for AI agents by implementing the DESIGN framework: make content Discoverable (structured data, schema markup), Explicit (clear definitions, unambiguous claims), Structured (semantic HTML, logical hierarchy), Intentional (defined scope, clear purpose), Governed (trust signals, author authority), and Navigable (breadcrumbs, cross-references, sitemap). Additionally, maintain vocabulary consistency, provide verifiable data with source attribution, and implement FAQ schema on every page.
Prioritise entity consistency across all content. AI answer engines build knowledge graphs from your content. If you use ' Build topical authority through content clustering. AI answer engines evaluate not just individual pages but the depth and breadth of a site's coverage of a topic. A single page about 'agentic banking' is less authoritative than a cluster of interconnected pages covering agentic banking, agent payments, Know Your Agent frameworks, financial services readiness, and trust architecture for banking. The AXD Institute's content architecture - Build a canonical vocabulary and use it consistently. The AXD Institute's