The Agent-Readable Web: llms.txt, Companion Files, and Credential Verification in the ARO Framework

This essay extends the Agentic Recommendation Optimization (ARO) framework to the emerging llms.txt standard and static markdown companion files. It proposes that curated markdown files, deployed alongside existing websites, can make licensed professionals structurally verifiable for autonomous AI agents that recommend service providers on behalf of users. The paper examines the token economics of HTML versus markdown delivery, documents adoption of the llms.txt standard across 2.2 million websites, distinguishes the approach from traditional search engine optimization, engages the strongest published counterarguments, and presents a falsifiable experimental design for testing whether markdown companion files measurably influence agent recommendation behavior. The application to credential verification for licensed local professionals is the novel contribution.