Cursor's planners, workers, and judge
Wilson Lin, Cursor · Catalog date October 16, 2026
Vendor source. Cursor's research blog, written by Wilson Lin, describes an internal harness for long-running coding: planners that can spawn sub-planners, workers that do not coordinate with each other, and a judge that decides whether another cycle should run. An earlier flat design with a shared file and locks was dropped.
Evidence. Sourced, incomplete
A public source is linked. Some results, prompts, or operating details are still unknown. This catalog has not re-run the crew.
- Goal
- Coordinate many coding agents on projects that run for days or weeks
- Use
- Software
- Platforms
- Cursor
- Crew size
- Multiple agents, count not stated
- Difficulty
- Not stated
Roles
This is Cursor's account of its own research, not an independent write-up. The post describes role types and says hundreds of workers run concurrently. It does not name individual agents. An integrator role was tried and removed.
Planner
Owns. Explores the codebase and creates tasks. Planners can spawn sub-planners for specific areas.
Constraints specific to this role
- To be added.
Worker
Owns. Picks up an assigned task, finishes it, and pushes the changes. Workers do not coordinate with other workers.
Constraints specific to this role
- Coordinate with other workers. The post states that as the role design.
Judge
Owns. At the end of a cycle, decides whether to continue. The next iteration then starts fresh.
Constraints specific to this role
- To be added.
Routines
Plan, work, judge
Each cycle
Planners create tasks, workers complete them, and the judge decides whether to run another cycle from a fresh start.
Guardrails
- Workers do not coordinate with each other and do not handle the big picture.
- Each new iteration starts fresh. The post says periodic fresh starts are still needed to counter drift and tunnel vision.
- The earlier design, where equal agents coordinated through a shared file and locks, was abandoned.
Handoffs
Planners create tasks. Workers pick those tasks up and push changes. They do not hand work to other workers. The judge decides whether the cycle continues. The post does not describe a message format between planner and worker.
- Planner
- Worker
- Judge
Human operator
Planners create tasks. Workers pick those tasks up and push changes. They do not hand work to other workers. The judge decides whether the cycle continues. The post does not describe a message format between planner and worker.
Results
- What worked
- The post reports a from-scratch browser in close to a week, with over 1 million lines across 1,000 files; a Solid-to-React migration in the Cursor codebase over three weeks (+266K/-193K) that was passing CI and still needed review; a Rust video path the post says was 25x faster and was merged; and other running experiments (Java LSP, a Windows 7 emulator, and Excel) with the commit and line counts given in the post. These figures are the vendor's report.
- What broke
- The shared-file lock design failed. Agents held locks too long, forgot to release them, or updated the file without the lock, and the lack of hierarchy led agents to avoid hard tasks. The post also says agents occasionally run for far too long. An integrator role for quality control and conflicts was removed because it became a bottleneck.
- Lessons
- The post says role separation and a fresh start each cycle addressed the coordination failures, that removing the integrator role was an improvement, and that prompts mattered more than the harness alone. It also says fresh starts are still required because drift remains.
Prompts
To be added.
Source
Wilson Lin, Cursor research blog, January 14, 2026. Vendor source.
https://cursor.com/blog/scaling-agents
Notes
This entry is a vendor source. Cursor is describing research on its own agent harness. The catalog has not re-run the experiments. The post is dated January 14, 2026. The catalog date is the day this entry was drafted.
The post also says hundreds of agents have worked on one codebase for weeks, that GPT-5.2 was better than other named models at long autonomous planning in their tests, and that the prompts are not included. Difficulty is not stated.