Short answer
Use the job being done, not the shortest acronym.
SEO remains the foundation for discovery in traditional and generative search. Answer Engine Optimization and Generative Engine Optimization describe work intended to improve visibility or citations in generated answers. Agentic Engine Optimization is an emerging documentation term for making technical sources usable by coding agents. Agent Search Optimization and Agentic Search Optimization extend the problem to whether an agent can discover, evaluate, and act through a website. RAG, query fan-out, llms.txt, MCP, and WebMCP are retrieval methods, files, or protocols—not replacement names for SEO.
01 · When to pay attention
The glossary is useful when one of these labels is hiding the actual deliverable.
- 01
A proposal promises AEO without saying whether the target is answer engines or autonomous agents.
- 02
A visibility report counts mentions but does not distinguish citations, source selection, referrals, or completed actions.
- 03
A team is asked to publish llms.txt or an MCP server before deciding what agents should be able to find or do.
- 04
GEO is presented as a replacement for SEO rather than a research-backed extension concerned with generative answers.
- 05
LLMO is used for both marketing visibility and model fine-tuning, evaluation, or inference optimization.
- 06
New acronyms are being used to package familiar work in crawl access, information architecture, technical writing, structured data, and reputation.
02 · Working method
I group the terms into four layers.
The layers overlap, but they are not interchangeable. A page must first be accessible and understandable. It may then be selected as a source for a generated answer. An agent may go further by comparing the offer or completing a task. Protocols can make that interaction more reliable, but they do not create authority or demand by themselves.
Search foundation
Search Engine Optimization (SEO), technical SEO, crawl access, canonical URLs, sitemaps, internal links, structured data, and useful content establish whether a source can be discovered, indexed, understood, and trusted. Google explicitly treats work for its generative search features as part of SEO.
Generated-answer visibility
Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), AI Search Optimization, and LLM Optimization are labels for improving the chance that a source or entity appears in an AI-generated answer. Their language and measurement are still inconsistent across research, platforms, and vendors.
Agent discovery and action
Agentic Engine Optimization, Agent Search Optimization, and Agentic Search Optimization describe agent-facing work. Depending on the author, the goal may be reliable documentation retrieval, brand evaluation, form completion, tool use, or an end-to-end transaction.
Retrieval, protocols, and measurement
Retrieval-augmented generation, grounding, query fan-out, llms.txt, MCP, WebMCP, citations, mentions, and AI share of voice describe mechanisms or measurements. They help explain how a system works or how visibility is observed; they are not standalone guarantees of ranking, citation, or agent adoption.
03 · Comparison
A practical taxonomy of the terms in use.
| Term | What it usually targets | How I would label it |
|---|---|---|
| SEO — Search Engine Optimization | Discovery, indexing, ranking, useful pages, and search demand across conventional and AI-assisted search. | Established foundation. Use it by default rather than announcing a replacement discipline. |
| AEO — Answer Engine Optimization | Clear, supported answers that can appear in search answer surfaces and generated responses. | Common emerging industry term. Google recognizes the expansion but considers the work part of SEO. |
| GEO — Generative Engine Optimization | Source visibility and citations inside responses generated by large language model search systems. | Research-backed emerging term introduced in a 2023 paper; evidence is promising but platform-dependent. |
| LLMO — Large Language Model Optimization | Usually brand or content visibility in LLM answers; elsewhere, model training, fine-tuning, compression, or inference. | Ambiguous practitioner label. Spell it out and define the target outcome every time. |
| AI Search Optimization / AISO | A broad umbrella for visibility across AI Overviews, chat search, answer engines, and related discovery products. | Understandable wording, unstable acronym. Useful as a category, not a technical specification. |
| GSO — Generative Search Optimization | A variant label for visibility in generative search results. | Less established than GEO and usually describes similar work. |
| AEO — Agentic Engine Optimization | Technical content, documentation, interfaces, and context that coding or task agents can reliably use. | Emerging practitioner term. Always write the full phrase because it collides with Answer Engine Optimization. |
| ASO — Agent Search Optimization | Whether agents acting for users can discover, evaluate, and take action through a website. | Emerging practitioner term. It also collides with the established App Store Optimization acronym. |
| Agentic Search Optimization | Brand discovery, evaluation, trust, and action in agent-mediated journeys. | Emerging industry variant used by Semrush and others; its boundaries remain fluid. |
| AI visibility / AI share of voice | Mentions, cited sources, cited pages, sentiment, and presence across a defined prompt set. | Measurement family, not an optimization method. The prompt set and collection method must be disclosed. |
| RAG and grounding | Retrieving external information to support a model response with current or authoritative context. | Established technical mechanism. It explains retrieval, not why one brand deserves selection. |
| Query fan-out | Generating several related searches to collect enough evidence for a complex question. | Documented retrieval behaviour in Google AI features, not a separate marketing discipline. |
| llms.txt | A proposed Markdown guide to a website and selected machine-readable sources. | Open proposal. It can package context but is not required by Google and does not guarantee discovery or citation. |
| MCP and WebMCP | Structured access to tools, data, prompts, resources, or browser actions for AI applications and agents. | Protocols and proposals for capability access. They solve interaction problems, not search authority. |
Practical sequence
Use four questions to translate any acronym into real work.
- 01
Name the surface
Is the target Google Search, an AI answer engine, a coding assistant, a browser agent, an internal retrieval system, or several of them? A term without a named surface is too vague to scope.
- 02
Name the desired behaviour
Separate being discovered, cited, recommended, evaluated, or used to complete an action. These outcomes need different pages, evidence, interfaces, and measurements.
- 03
Name the mechanism
Identify whether the work changes crawl access, content, entity signals, third-party evidence, structured data, context files, an API, an MCP server, a form, or a transactional workflow.
- 04
Name the evidence
Record the prompt set, engines, date, retrieved or cited URLs, referral traffic, action success, and known limitations. Measure the outcome before arguing about the acronym.
The shortest useful decision guide
Choose the term that matches the next observable outcome.
If the goal is durable discovery in Google and other indexed search products, call the work SEO. If the goal is selection as a source in generated answers, describe it as GEO or Answer Engine Optimization and define the engines and citations measured. If the source is technical documentation consumed by coding agents, Agentic Engine Optimization is a useful narrow label. If agents must compare an offer or complete a task, describe agent readiness or Agent Search Optimization and test the journey. Use llms.txt, MCP, or WebMCP only when their specific context or interaction model solves a real problem.
Primary sources
Research, official guidance, and practitioner definitions.
Questions
What to settle before you invest in AI-search work.
Does AEO mean Answer Engine Optimization or Agentic Engine Optimization?
Both expansions are now in use. Google uses AEO for Answer Engine Optimization. Addy Osmani and some technical-documentation practitioners use it for Agentic Engine Optimization. Do not publish the acronym alone: spell out the full phrase on first use and name the system or audience being optimized.
Is GEO replacing SEO?
No. GEO focuses on visibility in generated answers, but the accessible, useful, authoritative pages that support it still depend on SEO foundations. Google explicitly treats optimization for its generative search features as SEO. Use GEO when the measurement concerns generated answers or cited sources, not as a universal replacement label.
Is LLMO an established standard?
No. LLMO is an ambiguous practitioner acronym. In marketing it often means improving brand or content visibility in LLM responses. In machine learning, similar wording can describe improving the model itself. Define the expansion, system, intervention, and metric rather than assuming a shared meaning.
Does llms.txt improve rankings or guarantee citations?
No. llms.txt is an open proposal for providing a concise Markdown guide and selected sources to language models at inference time. Google says special AI text files are not required for its generative Search features. Use llms.txt when it provides useful curated context and test whether the intended tools consume it.
Are MCP and WebMCP forms of SEO?
No. MCP is a protocol for exposing data, tools, prompts, and resources to AI applications. WebMCP is an emerging browser-oriented proposal for exposing website actions as tools. They can support agent use after discovery, but they do not replace crawlability, content quality, authority, or demand.
What should I call this work on a proposal or service page?
Lead with the outcome: AI search visibility, cited-source improvement, technical-documentation retrieval, or agent-ready transactions. Add the acronym only after defining it. A clear scope should name the surfaces, prompts, pages, evidence, changes, and measurements.