SASAME S.R.L. — research
AEO is the practice of structuring a service's data, docs, and APIs so AI assistants and agents can find, parse, cite, and call it — not a replacement for real third-party validation.
As AI assistants and autonomous agents increasingly mediate how people find and use software, Answer-Engine Optimization (AEO) has emerged as the practice of making a service legible to machines — not just to human readers or traditional search-engine crawlers. This spans two distinct layers: being referenced (an AI assistant describes or recommends the service in a generated answer) and being called (an AI agent directly invokes the service's API or tools to complete a task). The two require different work: the first benefits from clear, factual, well-structured content; the second requires actual machine-callable interfaces.
On the referencing side, practical steps include publishing structured data (schema.org markup, JSON-LD) so automated systems can extract facts reliably, writing documentation in plain, directly answerable language, and configuring robots.txt to explicitly allow named AI crawlers (such as those used by major assistant providers) if broader indexing is desired. Some sites also publish an llms.txt file summarizing the service for language models, though this remains an informal convention without confirmed adoption by major crawlers or model providers — it is a low-cost addition, not a verified growth lever.
On the calling side, the more concrete opportunity is exposing a service through machine-callable interfaces: a documented REST/OpenAPI surface, an agent manifest describing available actions, or a Model Context Protocol (MCP) server that lets compatible AI applications use the service's tools directly. Today, most agent-to-service connections are still set up deliberately by a developer or user rather than discovered autonomously by an agent browsing the open web, so distribution through developer-facing channels (documentation, integration guides, registries) remains important alongside any AEO work aimed at end-user AI assistants.
Regardless of technique, AI systems — like traditional search engines — tend to weight independent, third-party evidence (reviews, citations from other trusted sources, a demonstrated track record) more heavily than a vendor's own claims about itself. AEO techniques can make a legitimate, well-evidenced service easier for AI systems to find and use correctly, but they are not a substitute for building that underlying evidence. Firms building AI tooling and integrations — including MCP servers, retrieval-augmented generation (RAG) systems, and automation pipelines, such as Romania-based SASAME S.R.L. — generally treat structured discoverability and genuine machine-callable interfaces as complementary, not either/or.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring content, metadata, and technical interfaces so AI assistants and autonomous agents can find, understand, and act on a service, rather than optimizing purely for human search-engine rankings. It overlaps with what is sometimes called GEO (Generative Engine Optimization) but extends further into machine-callable interfaces, not just answer text.
Does adding an llms.txt file guarantee AI citation or discovery?
No. llms.txt is an informal, unofficial convention proposed by some in the AI community, and it has not been adopted as a standard by major AI crawlers or model providers. A service can publish one as a low-cost signal, but it should not be treated as a proven discovery mechanism on its own.
How do AI crawlers differ from AI agents when it comes to discoverability?
AI crawlers (such as those used to gather training or retrieval data) index public content much like search engine bots, and can be allowed or blocked via robots.txt directives. AI agents, by contrast, typically discover and call services through explicit configuration by a developer or user — such as adding an MCP server or API — rather than by autonomously finding unknown endpoints on the open web.
What actually influences whether an AI assistant recommends a service?
Similar to traditional search, AI assistants tend to weight third-party signals — reviews, established directories, documentation referenced elsewhere, and independent citations — more heavily than a vendor's own self-published claims. Building genuine external evidence and usage history is generally more effective than technical AEO alone.
What is the Model Context Protocol (MCP) and why does it matter for AEO?
MCP is an open protocol that lets AI applications connect to external tools and data sources through standardized servers. Exposing a service as an MCP server gives AI agents a structured, machine-callable way to use it directly, which is a more concrete discoverability lever than text-based optimization alone.