Robinhood Chain markets itself around agentic trading — the idea that autonomous agents, not just humans, will be first-class participants. That makes agent-readable data a core design concern rather than a novelty.

Why agent-readability matters here

An AI agent evaluating tokenized-stock dislocations needs the same thing a human does — but structured. That means:

  • A plain-text llms.txt describing the feeds and their exact semantics (what premium_bps means, when the oracle freezes).
  • An agents.md with the “verify, don’t trust” essentials: the agent’s public wallet, the boundary of its autonomy, how to consume the data.
  • Structured data on every page (Dataset, FAQPage) so a crawler ingests the measurement, not just prose.

The credibility bar

The last cycle of “AI agent” projects collapsed because most were wrappers making claims they could not back. The bar now is simple and high: a public wallet you can watch, honest disclosure of where the human sits, and real execution rather than a marketing narrative. See how Rusty works for exactly where the automation ends and the human begins.