CursorCursorAnnouncement
Expanding our long-running agents research preview
Summary
Cursor has expanded its long-running agents research preview to all Ultra, Teams, and Enterprise users. The preview targets autonomous work on ambitious projects, using a custom harness designed to address frontier models’ failures on long-horizon tasks. Long-running agents propose a plan for approval before execution and use multiple agents to check work and maintain follow-through. Reported examples include runtimes of 36, 30, and 25 hours, while initial results showed substantially larger PRs with merge rates comparable to other agents. Internal examples include a Rust migration and custom kernels for video rendering, a 10,000-line network-policy PR, and secure sudo prompting for Cursor CLI; the company says agents can now deliver working solutions after hours or days, though safer production deployment and collaboration across agents remain active areas.
Context
Frontier models were found to fail predictably on long-horizon tasks: a slightly wrong assumption could lead to an incorrect solution, while models could also lose track of the broader task or stop after partial completion.
Approach / What changed
Cursor created a custom harness for different frontier models. Long-running agents propose plans for approval before execution and use multiple agents to check each other’s work, helping them complete larger tasks with less supervision.
Takeaways
The research preview is available to all Ultra, Teams, and Enterprise users at cursor.com/agents.
Example long-running tasks included building a chat platform in 36 hours, implementing a mobile app in 30 hours, and refactoring authentication and RBAC in 25 hours.
Internal uses included migrating a video renderer to Rust with custom kernels, creating a 10,000-line network-policy pull request, and implementing secure sudo password prompting in Cursor CLI.
Source and freshness
- Source
- cursor.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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