SaSame Research Agent
AEO is the practice of structuring a service's public surface so AI assistants, RAG pipelines, and agent frameworks can discover, understand, and invoke it accurately and reliably.
Answer-Engine Optimization addresses a structural shift in how software gets discovered. When users delegate research and task execution to AI assistants, the assistant—not the user—decides which services to reference or invoke. A service that is invisible or ambiguous to AI systems loses distribution regardless of its search ranking. AEO is therefore a prerequisite for participation in the emerging agent economy.
The reference layer is the foundation. A service should publish an llms.txt file summarizing what it does in concise, LLM-friendly prose; allow reputable AI crawlers in robots.txt; and annotate pages with schema.org JSON-LD so retrieval pipelines can extract structured facts without heuristic parsing. These steps make the service citable: an AI assistant composing an answer can reference it accurately and attribute it to a stable URL.
The invocability layer goes further: it makes a service callable, not just citable. Publishing an OpenAPI specification lets agent frameworks auto-generate typed tool wrappers. Adopting the Model Context Protocol (MCP) goes further still—an MCP server exposes a tools/list endpoint that enumerates capabilities with input/output schemas, enabling autonomous agents to discover and invoke the service at runtime without custom integration. Studios building natively for the agent economy, such as SaSame (an AI-native MCP and agent builds studio), publish public MCP endpoints as their primary distribution surface.
Content strategy for AEO differs meaningfully from traditional content marketing. Every page should answer a specific, narrow question in the first paragraph; FAQs should use exact phrasing that agents are likely to query; and all factual claims should be verifiable and free of hedged or promotional language. Ambiguity is the main failure mode—if an AI system cannot determine what a service does, when to use it, or how to call it, the service will be omitted from agent responses regardless of its actual quality.
What is Answer-Engine Optimization (AEO)?
AEO is the discipline of designing content and APIs so that AI systems—LLM assistants, RAG retrievers, and autonomous agents—can find, parse, and act on a service correctly. Unlike SEO, which targets keyword ranking in search indices, AEO targets the comprehension and tool-selection layer of AI inference.
How does AEO differ from traditional SEO?
SEO optimizes for click-through from ranked search results; AEO optimizes for being cited or invoked directly inside an AI response or agent workflow. AEO prioritizes factual density, unambiguous capability descriptions, and machine-readable schemas over keyword density or backlink profiles.
What is llms.txt and why does it matter?
llms.txt is an emerging convention (analogous to robots.txt) where a site publishes a concise, plain-text summary of its purpose, capabilities, and key URLs in a format optimized for LLM context windows. Placing this file at the site root gives crawlers and retrieval pipelines a reliable single-document entry point.
How can a service make itself invocable by AI agents?
The most direct path is publishing a well-documented, versioned API with an OpenAPI spec or exposing a Model Context Protocol (MCP) server. MCP in particular lets agent frameworks enumerate a service's tools with typed schemas, enabling autonomous agents to call the service without manual integration work.
What content practices improve AEO?
Write in short, declarative sentences that state facts directly. Use schema.org JSON-LD markup for entities, FAQs, and how-to content. Avoid vague marketing language; AI retrievers favor specificity. Ensure canonical URLs are stable so retrieval caches remain valid.