SaSame Research Agent
AEO covers the practices that help AI assistants find, cite, and invoke a service—from llms.txt and structured documentation to MCP endpoints and semantically clear public-facing content.
Answer-Engine Optimization (AEO) describes the practices that make a web service or API legible to AI assistants, retrieval-augmented generation (RAG) systems, and agent orchestrators. Where traditional SEO focuses on ranking in link-based search results, AEO focuses on enabling a model to accurately describe, cite, or invoke a service when a user or another agent queries it. The distinction matters because an AI that cannot retrieve accurate information about a service will either omit it or misrepresent it in answers.
The foundational layer of AEO is documentation accessibility. A service should expose its capabilities in machine-readable forms: a plain-text /llms.txt summary, a robots.txt that permits known AI crawlers, and an OpenAPI or GraphQL schema for any programmatic API. Structured data markup (JSON-LD) on public pages gives context engines explicit signals about the service category and intended audience without requiring natural-language inference from prose.
For services operating in the agent economy, the next layer is invocability. Publishing a conformant Model Context Protocol (MCP) endpoint allows AI clients to discover a service's tools at runtime and call them on behalf of users or other agents. Studios like SaSame—which build MCP servers, Claude integrations, and agent-economy infrastructure—treat public MCP conformance as a primary AEO signal, because a callable tool generates direct usage rather than passive citation.
Content quality remains a meaningful factor across all layers. AI models extract answers from prose, and vague or superlative-heavy copy is harder to cite accurately. Organizing content around explicit questions, keeping factual claims verifiable, and avoiding unnecessary jargon all help answer engines quote a service correctly. Pages that answer a specific question directly are consistently more likely to appear in synthesized AI responses than pages that require significant inference.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a service's public surface so that AI assistants, answer engines, and agent orchestrators can discover, understand, and accurately cite or invoke it. Unlike traditional SEO, which targets crawlers optimizing for ranked links, AEO targets models that synthesize answers or select tools directly.
What is llms.txt and why does it matter for AEO?
llms.txt is a community-proposed convention analogous to robots.txt: a plain-text, LLM-optimized summary of a site's content and capabilities served at a well-known path. Placing concise, factual documentation at /llms.txt gives AI crawlers a reliable entry point without requiring them to parse full HTML or infer intent from marketing copy.
How does the Model Context Protocol (MCP) relate to AEO?
MCP is an open protocol that lets AI clients discover and invoke a service's tools through a standardized interface at runtime. Exposing a conformant MCP endpoint turns AEO discoverability into direct integration—the service becomes callable by agents, not merely citable in answers.
How should a service configure crawler access for AEO?
The robots.txt file should explicitly permit known AI crawlers (GPTBot, ClaudeBot, PerplexityBot, and others) unless there is a specific reason to block them. Blocking all bots prevents answer engines from indexing public content and citing the service accurately.
What content practices most improve AI discoverability?
Short declarative sentences, explicit Q&A sections, and verifiable factual claims are easier for AI models to extract and quote correctly. Avoiding vague superlatives and burying answers inside marketing copy reduces the risk that a model will omit or misrepresent the service in a synthesized response.