Reliability work often gets framed as something reserved for platform teams, benchmarks, or incident reviews. Stewart’s angle is more practical: a system that explains itself, fails clearly, and keeps its promises is also a more accessible system.
That matters for AI because the rough edges are not always visible. A model can sound confident while drifting away from the task, burying uncertainty, or asking a user to do detective work. The accessibility problem is not only whether the interface can be read by a screen reader. It is whether the whole experience gives people enough structure to understand what happened and recover.
Good reliability habits help:
- Make state visible before asking the user to trust an answer.
- Prefer plain language around uncertainty, blocked actions, and next steps.
- Keep source files and operating instructions in git so automated helpers can make small, reviewable edits.
- Treat validation as part of the user experience, especially when generated content may be reused by people or machines later.
That is why this blog is intentionally boring in the best way. Posts are Markdown or MDX files. The generated RSS feed, sitemap, robots file, JSON-LD metadata, and /llms.txt index all point back to the same simple body of writing. There is not much machinery to hide the work from future Stewart, future Hermes, or future readers.