Cursor

AI-native coding agent that plans and executes multi-step changes in parallel, with multi-model support and cross-repo context. Available as desktop app and CLI.

AI Developer Tools#Programming#IDE#AI Agent#Editor

Updated: 2026-10-26

What is Cursor?

Cursor's official definition describes it as an "AI Coding Agent" rather than an ordinary code editor — the page title directly states "Built for building ambitious software." Its working method is to take on tasks as a whole: independently planning steps, advancing in parallel, and executing multi-step modifications across files. You are responsible for defining goals and reviewing results, letting the agent turn ideas into code. The official claims that teams building world-class software use it every day; behind Cursor is an applied research team that calls itself "dedicated to building the future of software development," with a fast product iteration rhythm and continuous follow-up of the cutting edge of model capabilities.

Who is it for?

Professional developers who are accustomed to the IDE workflow and hope to hand over large programming tasks; engineering teams with parallel multi-repository and multi-task operations; as well as individuals and teams who want to use intelligent agents to handle repetitive coding work. The editor itself is based on the VS Code lineage, with low migration costs for plug-ins, shortcut keys and configurations, and it can seamlessly integrate into existing habits, which is also an important reason for its low entry threshold.

Key features

Agent-Driven Development

Hand over tasks directly to the agent, which plans steps on its own, executes multi-step changes, and provides a change preview including lines added or deleted, completing the decomposition and implementation of complex tasks in one go.

Parallel Tasks & Mission Control

Multiple tasks can run simultaneously. The Mission Control interface centrally displays the progress, plans and results of each task, making it convenient for you to schedule priorities and intervene or correct the direction at any time. Officially, this is called "working independently and running in parallel".

Rule & Behavior Triggering

Supports configuring project-level Rules, and controlling the observation and execution behaviors of Agents through mechanisms such as Triggers and View Behavior, enabling Agents to abide by code specifications, directory conventions, and established workflows for a long time.

Covers all tools and goes through every step

The official emphasizes "covering all tools and going through every step." The AI agent is involved in everything from the terminal and diff to code reviews; it can also automatically handle repetitive tasks and support real-time synchronization (Realtime Sync) status.

Select the best model for each task

No single model is bound; you can pick the most suitable model based on task types. It supports All Repos cross-repository projects, and provides two interfaces: Cursor Desktop and Cursor CLI, covering both graphical and pure terminal working habits.

How to use Cursor

First, clarify the task boundaries and acceptance criteria, prepare the rule files for the project, and then let it execute autonomously.
When introducing the team, adjust the code review process simultaneously.
otherwise, the review will become a new bottleneck.
It is recommended to start with a single repository and small tasks, and gradually unlock the parallel and cross-repository capabilities after running through.
The official provides two forms: the macOS desktop version and the CLI, which can be selected according to your own operating habits.

Things to know

The output generated by the Agent must be manually reviewed, and special attention should be paid to line-by-line checks when it comes to dependency upgrades and large-scale refactoring to prevent "automation" from turning into "loss of control".

Autonomous parallel tasks will significantly consume model call quotas, so teams should pay attention to costs and rate limits when using it on a large scale.

The quality of the rule files directly determines the stability of the Agent's behavior; if the rules are written ambiguously, the results will be difficult to reproduce.

Deep usage requires some adaptation period to the differences from native VS Code (such as Agent-related shortcuts and interface layouts).