Cursor Agent

Cursor’s bet is that the review surface matters as much as the model. Everything the agent does arrives as a proposed diff you accept, reject, or amend, which keeps the editor as the source of truth rather than a chat transcript.

Review as a first-class stage

Most agent failures are not failures of generation. They are failures of review — a change that looked reasonable, was approved quickly, and turned out to be wrong three days later.

By routing every agent action through the editor’s diff surface, Cursor keeps review where developers already do it well: side by side, with syntax highlighting, with the surrounding code visible, with the ability to edit the proposal rather than accept or reject it wholesale. That last capability matters more than it sounds. Being able to take eighty percent of an agent’s change and fix the rest by hand is far more useful than a binary verdict.

The cost is that the human stays in the loop by construction. This is a supervised agent, and it does not pretend otherwise.

Retrieval and rules

The editor maintains an index of the project so a request like “add rate limiting to the public endpoints” can locate the relevant handlers without you listing them. On a codebase you know well this saves typing; on one you do not, it is the difference between a usable request and an impossible one.

Rules files are the underused half. Pinning conventions — directory layout, testing style, forbidden dependencies, how errors are wrapped — stops the agent relearning them each session and stops it inventing plausible alternatives. Teams that maintain rules files get consistently better output than teams that re-explain conventions in every prompt, and the gap widens as the project grows.

MCP support extends the tool surface beyond the editor, so the agent can reach a database, an issue tracker or an internal service through the same protocol other clients use.

Where it breaks down

Retrieval degrades on very large monorepos, and it degrades silently. The agent surfaces a similarly named module from an unrelated service, builds a coherent change on it, and nothing signals the mistake. Naming files explicitly is the mitigation, and it is worth doing habitually on unfamiliar code.

Long autonomous runs drift. The agent works best on a task you could describe in a paragraph and worst on one that requires judgment about trade-offs it cannot see — which of your existing compromises are deliberate, which parts of the system are load-bearing.

Usage-based metering on heavier model calls means an enthusiastic agent user costs several times a light one. That is a budgeting consideration rather than a criticism, but it needs measuring during a pilot rather than estimating afterwards.

Where it fits

Feature work and refactors inside a project you already know, done at editor speed, with a human reviewing each step. This is the shape most working developers actually want most days: meaningful leverage without handing over control.

Teams that want fully unattended runs in CI will find a terminal or cloud agent a better shape — Claude Code for a scriptable terminal loop, Devin for asynchronous ticket-to-pull-request work. Teams that need model portability or self-hosted inference need Aider.

See our Cursor vs Windsurf comparison for how it lines up against the closest alternative.