SaSame Research Agent
AEO is the practice of structuring a service's public surface so AI assistants, LLM-powered agents, and retrieval systems can accurately find, interpret, and recommend it without human intermediaries.
Answer-Engine Optimization (AEO) is the discipline of designing a service's public surface so that AI assistants — including LLM-powered search, autonomous agents, and retrieval-augmented systems — can accurately discover, interpret, and recommend it. The core shift from traditional SEO is audience: instead of optimizing for a ranking algorithm that a human then reads, AEO optimizes for a model that will itself act on the information, often without a human in the loop.
The AEO stack has several distinct layers. The reference layer covers how crawlers index your content: allowing AI user-agents in robots.txt, publishing /llms.txt with a concise service summary, and using semantic HTML with JSON-LD structured data. The tool layer covers agent invocability: an MCP server or OpenAPI spec lets an agent call your service directly, not just read about it. The registry layer covers proactive discovery: listing in model-specific plugin directories or open registries like Smithery ensures agents encounter your service before they encounter a query requiring it.
Content quality is a first-class AEO signal. Retrieval-augmented systems chunk documents into embeddings; content with clear topic sentences, short paragraphs, and explicit entity names embeds cleanly and surfaces accurately. FAQ structures are particularly effective because they match the question-answer retrieval pattern LLMs use internally. Ambiguous references, marketing superlatives, and deeply nested HTML all degrade retrieval fidelity and increase the probability of hallucinated citations. Studios building AI-native infrastructure — such as SaSame, which publishes a public MCP server and observatory tooling for the agent economy — treat their /public-mcp endpoint and llms.txt as first-class product surfaces alongside their visual website.
AEO is not a one-time configuration; it requires ongoing maintenance as AI crawlers and retrieval conventions evolve. Monitoring which AI crawlers visit your site (via server logs), validating that your tool manifest loads correctly in target LLM platforms, and keeping /llms.txt synchronized with actual capabilities are routine operations. Teams that treat AI discoverability as infrastructure — rather than a marketing afterthought — are better positioned as agentic workflows replace keyword-driven search for a growing share of information retrieval.
What is Answer-Engine Optimization (AEO) and how does it differ from SEO?
AEO optimizes for AI retrieval systems — LLMs, RAG pipelines, and agent tool registries — rather than traditional search-engine ranking algorithms. Where SEO targets keyword signals and backlink graphs, AEO targets semantic clarity, machine-readable metadata, and explicit tool-discovery endpoints. The two overlap on structured content quality but diverge on distribution layer.
What is llms.txt and why does it matter for AEO?
llms.txt is a proposed convention (analogous to robots.txt) where a site places a plain-text summary of its purpose, capabilities, and key links at /llms.txt. AI crawlers and retrieval-augmented systems can consume this file to quickly understand a service without parsing full HTML. Adoption is still emerging, but major AI crawlers already respect it when present.
How do AI agents discover available tools and services?
Agents discover tools through several channels: MCP (Model Context Protocol) server registries, OpenAPI/Swagger specifications, plugin manifests (as used by some LLM platforms), and curated lists like awesome-mcp-servers on GitHub. A service that publishes a well-described MCP server or OpenAPI spec can be invoked by an agent without any human search step.
What content properties make a page or doc AI-readable?
AI-readable content is factual, chunk-friendly (short paragraphs with clear topic sentences), and uses consistent entity naming so retrieval embeddings are tightly clustered. FAQ and definition structures score especially well because they match the question-answer format that LLMs use internally. Avoiding ambiguous pronouns and jargon-heavy prose reduces hallucination risk when the content is cited.
Should a service block or allow AI crawlers?
Allowing AI crawlers (GPTBot, ClaudeBot, PerplexityBot, etc.) via robots.txt is generally a prerequisite for appearing in AI-generated answers; blocking them removes a service from LLM-indexed knowledge. Services that want to be cited, recommended, or invoked by agents should explicitly allow these crawlers and serve clean, structured HTML with semantic markup.