Docs built for humans, broken for AI agents

I keep hearing the same complaint from developers building AI agents: their docs don't work. Not because the content is wrong, but because it's structured for humans—for navigation, not for retrieval by an LLM trying to use it autonomously.

AgentDocs caught my attention because it's a conversion layer, not just another doc platform. It takes what you already have—Markdown, Notion, Confluence, OpenAPI specs—and transforms them into agent-consumable formats: structured JSON, semantic chunk hierarchies, retrieval-optimized embeddings. The mechanism is concrete. You're not replacing your docs, you're reformatting them so agents can actually use them.

Here's the angle I'm sitting with: when an agent hallucinates an API call, you currently spend hours debugging whether it's the prompt, the model, or the retrieval. With docs structured for agents from day one, that whole class of debugging shrinks. The emotional payoff is knowing your docs are correct by construction.

The micro SaaS model feels clean: $29–$99/month for indie devs and small teams, $299–$999/month for enterprise. Start in LangChain Discord and agent dev Slack groups—the exact people who've already hit this wall.

I'm genuinely uncertain about timing here. Is this pain urgent enough that devs will pay now, or does it need the ecosystem to mature first?