Agents already use docs as product instructions
Coding agents, AI search products, and automated support flows read docs long before a human asks for help. If the docs are hard to fetch or parse, the product feels harder to use.
Run the benchmark on any public docs site and see whether an agent can find the right page, read clean text, and follow the instructions without guesswork.
Try one of these public docs URLs:
Browse public benchmark reports, compare category scores, and open the full breakdown for each docs site.
Showing 25 of 171 matching docs.
This is not a vanity score. It measures whether an agent can discover, read, and act on your documentation.
Coding agents, AI search products, and automated support flows read docs long before a human asks for help. If the docs are hard to fetch or parse, the product feels harder to use.
Agent-readable docs shape implementation speed, support load, and whether a product feels trustworthy during evaluation.
A shared benchmark gives teams a concrete way to compare docs quality, spot weak areas, and track improvements over time.
This scoring model is informed by AFDocs and adapted into a public benchmark teams can inspect, compare, and rerun.
Read the open standard for agent-friendly documentation.
Inspect the source and contribute to the spec directly.
Follow the work and background behind the AFDocs effort.
Checks for llms.txt, sitemaps, and a clear public docs entry point.
DocsAlot helps teams improve help centers, developer docs, API docs, and CLI docs so they are easier for humans to use and easier for agents to read.