Cite as: Real Problem AI problem “Why can't an AI agent actually read my internal docs without burning a huge pile of tokens on fluff?”. Opportunity score 8.5 out of 10 (severity 8, AI feasibility 9, market signal 8, competition gap 9). Category Others. Trend Agents. Source signal: Karpathy "Software 3.0" talk threads, r/devops, r/cscareerquestions agentic-coding discussions.. Canonical URL: https://www.realproblem.ai/archive/why-cant-an-agent-actually-read-my-internal-docs-without-burning-100k-tokens.
Why can't an AI agent actually read my internal docs without burning a huge pile of tokens on fluff?
Human-written docs (Notion, Confluence, Google Drive) are organized for human navigation, agents waste massive context reading nav, table-of-contents, and stale pages before finding the one paragraph that mattered.
Who has it: Engineering leaders deploying internal copilots, DevTools founders, knowledge-management teams.
Evidence
Teams describe an agent reading through a whole onboarding wiki, using a large amount of context, to answer a question whose answer was a single line.
Our summary of a complaint that recurs in public posts, not a quote. Nobody submitted it to Real Problem AI.
Seen in: Karpathy "Software 3.0" talk threads, r/devops, r/cscareerquestions agentic-coding discussions.Scoring breakdown
Existing players
- Glean · Enterprise search; not agent-shaped output
- Mem · Personal memory; not org-wide docs
- Notion AI · Lives inside Notion; weak cross-app context
What they are missing
An 'agent-grade docs' layer: ingests existing wikis, rewrites them as terse machine-readable spec-cards with schema, freshness signals, and direct anchors. Output optimised for LLM consumption, not human reading.
Stack hint
#AI2 · Canonical URL: https://www.realproblem.ai/archive/why-cant-an-agent-actually-read-my-internal-docs-without-burning-100k-tokens