1 min lesson
A mode-two automation in the wild: the self-maintaining knowledge base
Connect "A mode-two automation in the wild" to the decision you would make.
Step 1 of 2
A mode-two automation in the wild: the self-maintaining knowledge basethe chore a human would never get to
Cursor's ops team runs an AI-driven ticketing system that leans on a detailed knowledge base, so the KB has to stay current or the answers rot. They seeded it by having Cursor scrape roughly six months of Slack history through the Slack MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. - learning what got asked, how it was answered and whether the answer actually resolved. Then they built the part that keeps it alive: an automation that runs every 28 days, reviews the prior 28 days of channel messages and proposes new KB entries. It drops a Slack message - here's what I found, do you want to approve these? - and a human clears the gate.
Auditing a knowledge base every month is exactly the task that dies on a human backlog. An automation does it reliably forever, and the approval step keeps a person on the hook for what lands. Note the pattern: it proposes, a human approves - the same shape you want on anything touching the books.
“That's something that you'd ask someone to do and they would just never do it... It would fall to the bottom of the priority list.”