AI Search & Agents
AI adoption also changes how businesses are found and selected. As AI systems summarize sources, choose tools, and act through agents, websites and interfaces need to become understandable to both humans and machines.
This talk explores a specific nuance of AI adoption: how websites and tools are found and selected in AI search and by agents. Practical examples show why clear content, explicit terms, credible sources, and structured data matter. It also shows how tool names, descriptions, parameters, and limits help agents make the right choice. Suitable as a conference talk, deep dive, or workshop.
SEO optimizes visibility in search systems, while GEO and LLMO focus more on how content is found, understood, cited, and summarized in generative answers. The labels overlap, but the structural work matters more than the acronym.
Useful work includes clear information architecture, precise answers, explicit entities, credible sources, structured data, good internal links, and content with real subject-matter substance.
Not necessarily. llms.txt can be an interesting orientation layer for LLMs, but it does not replace clean site structure, understandable content, or technical accessibility.
MCP concerns how agents use tools and data sources. Once AI systems do more than read content and start choosing tools, names, descriptions, parameters, and constraints become part of the visibility and interface question.
A good tool description states purpose, suitable use cases, parameters, limits, exclusions, side effects, and examples. It helps models choose the right tool and avoid misunderstood calls.
