Agentic Resource Discovery and dotdog
Why agent discovery needs clean indexes, machine-readable metadata, and stable resource maps.
Agents need to find the right resource before they can reason about it. A human can tolerate a messy site by guessing, searching, or clicking around. An agent works better when the site declares its important pages directly.
That is the practical value of agentic resource discovery: give models and tools a stable map of what exists, what each page means, and where the canonical source lives.
What discovery requires
Discovery starts with crawlable pages and stable URLs. It improves with canonical links, page descriptions, sitemap coverage, and a concise llms.txt that names the project surfaces in plain language.
The goal is not keyword stuffing. The goal is consistency. If dotdog is the spec compiler, collar is the DAG-first harness, leash is the robotics harness, and dogbench is the benchmark surface, every public index should say that in the same way.
Where dotdog fits
dotdog gives projects a structured source of truth. The website then exposes that source of truth to humans, search engines, and generative engines. This makes the site easier to cite, easier to crawl, and easier for an agent to use as a starting point.