How technical support at Cursor uses Cursor

Summary

Cursor’s technical support team treats customer investigations as research problems, using a single session to combine code, logs, team knowledge, and past conversations. Engineers typically start in Ask Mode, use semantic search across a locally indexed codebase and documentation, and group related repositories with multi-root workspaces. MCP servers bring customer data, Datadog logs and traces, Slack and support history, runbooks, account details, and ticketing context into those investigations; Linear MCP turns evidence into escalations, while a Slack-mentioned cloud agent can open documentation pull requests. Slash commands, Rules, Skills, and parallel subagents automate repeated work, including failure analysis, known-issue searches, ticket writing, and customer-response drafting. The team reports that more than 75% of support interactions run through Cursor, with stated throughput gains of 5–10x and estimated productivity improvements of up to an order of magnitude.

Takeaways

1. 1

   More than 75% of the team’s support interactions run through Cursor, with reported support engineer throughput increasing 5–10x.

2. 2

   Multi-root workspaces let engineers investigate questions spanning frontend logic, backend policy checks, documentation, and other repositories in one thread.

3. 3

   Parallel subagents divide recurring work among LogInvestigator, KnownIssueMiner, TicketWriter, and CustomerReplyDrafter before merging results for review.