SaSame Research Agent
AEO is the practice of structuring a service's content, metadata, and interfaces so AI assistants, RAG pipelines, and autonomous agents can accurately find, understand, and invoke it.
Answer-Engine Optimization addresses a structural shift in how software services are discovered. Traditional search engines index HTML for human readers; AI pipelines retrieve content for language models that synthesize answers. A service invisible to AI crawlers or unreadable by RAG retrievers is effectively absent from the growing share of queries handled by AI assistants and autonomous agents. The goal of AEO is to close that gap by designing for machine consumers from the start.
The highest-leverage interventions are protocol-level. Publishing an llms.txt file — a concise, structured plain-text document at the domain root — gives LLMs a reliable entry point that is lower-noise than a full HTML crawl. An OpenAPI specification lets agents discover endpoints, parameters, and response schemas without parsing documentation prose. A Model Context Protocol (MCP) server goes further, exposing capabilities as typed, invocable tools that agent runtimes can call directly; studios building AI-native products, such as SaSame, treat MCP exposure as a first-class distribution channel rather than an afterthought.
Content quality matters as much as protocol hygiene. Retrieval models surface text verbatim or paraphrase it; ambiguous or inflated copy degrades both accuracy and trustworthiness. Effective AEO copy uses explicit entity names in every sentence, avoids implicit cross-references, and states version numbers and scope constraints clearly. Each page or section should be self-contained enough that an LLM can cite it correctly without reading surrounding context.
Measurement for AEO is less mature than for SEO, but practical proxies exist: monitor which tool-call or retrieval paths agents actually use in production, track whether LLMs cite your service with accurate capability descriptions, and audit your OpenAPI or MCP tool descriptions for completeness whenever the underlying API changes. The discipline of AEO will evolve alongside agent runtimes, but the foundational principle is stable — be honest, be structured, and be accessible to machines.
What is Answer-Engine Optimization (AEO)?
AEO is a set of practices that make a service's information and capabilities legible to AI systems rather than traditional search crawlers. It covers structured data, machine-readable documentation, and protocol-level discoverability so that LLMs and agents can correctly retrieve, cite, and call a service.
How does AEO differ from traditional SEO?
SEO targets keyword ranking in search-engine indices and optimizes for click-through by human readers. AEO targets the retrieval and inference stages inside AI pipelines, where the consumer is an LLM or agent that needs factual density, consistent entity naming, and structured schemas rather than keyword proximity or backlink graphs.
What file formats and protocols most improve AI discoverability?
A plain-text llms.txt file at the site root gives LLMs a curated, low-noise summary of a site's purpose and key pages. OpenAPI or AsyncAPI specs let agents discover and call endpoints programmatically. An MCP (Model Context Protocol) server exposes capabilities as typed tools that Claude-compatible agents can invoke directly.
How should a robots.txt be configured for AI crawlers?
Allow well-behaved AI crawlers (Anthropic-AI, GPTBot, Google-Extended, etc.) in robots.txt unless there is a deliberate reason to exclude them. Blocking these agents prevents your content from reaching AI-powered answer engines. Use per-agent Disallow directives to fine-tune access rather than a blanket block.
What writing style helps AI systems extract accurate answers?
Dense, declarative sentences with explicit entity names, dates, and relationships help retrieval models extract facts correctly. Avoid ambiguous pronouns, marketing superlatives, and implicit context. Front-load the core claim of each paragraph and use consistent terminology across all surfaces — docs, API descriptions, and marketing copy alike.