Most developers don’t want to leave their editor to use AI — they want the AI inside it. That’s the premise behind Cursor, one of the most widely adopted AI-powered code editors, built as a fork of Visual Studio Code with AI woven directly into the editing experience rather than bolted on as a separate panel or plugin.
What Is Cursor?
Cursor is a code editor, built on the same open-source foundation as VS Code, with AI features integrated throughout: inline code completion, chat with the codebase, and agent-style edits that can modify multiple files at once. Because it’s a full editor rather than a browser tab or a terminal tool, it keeps the AI in the same window where the code already lives — file tree, terminal, and extensions included — which is a meaningfully different experience from switching between an editor and a separate chat interface.
How Cursor Works
Cursor offers a few distinct ways to use AI while coding, and most developers move between them depending on the task:
- Tab completion. As you type, Cursor predicts not just the next few characters but entire multi-line edits, often anticipating changes elsewhere in the file based on an edit you just made.
- Inline edit. Select a block of code, describe the change in plain language, and Cursor rewrites just that selection in place.
- Chat. A conversational panel that can answer questions about the codebase, pulling in context from the files you’re working with rather than requiring you to paste code in manually.
- Agent mode. The most autonomous option: give Cursor a broader instruction and it will plan the change, edit multiple files, run terminal commands, and iterate — the same underlying pattern used by dedicated AI agents like Claude Code, but packaged inside a traditional editor interface.
Model-Agnostic by Design
One detail that sets Cursor apart from many AI coding tools is that it isn’t tied to a single AI lab’s model. Developers can typically choose which underlying model powers a given request, switching between options from different providers depending on the task, cost, or personal preference. That’s a deliberate contrast with the kind of single-vendor lock-in discussed in our piece on vendor lock-in with AI tools: rather than betting entirely on one model’s strengths and weaknesses, Cursor lets teams swap the engine without swapping the editor around it.
Cursor vs. Other Agentic Coding Tools
Cursor sits in an interesting middle position in the current landscape of AI coding tools. Compared to a terminal-first tool like Claude Code, Cursor keeps more of the workflow visual and editor-centric — you watch the diffs appear in familiar file tabs rather than reading a command-line transcript. Compared to fully autonomous systems in the style of Devin, which are built to take a ticket and work through it largely unsupervised, Cursor still assumes a developer is actively driving: reviewing suggestions, approving agent actions, and staying inside the loop rather than handing off a task and walking away. It’s closer to the multi-agent coordination pattern used by more autonomous tools, but scaled down to fit a single developer’s active editing session.
What Makes a Good Code Editor “AI-Native”
A few design choices show up repeatedly in editors built AI-first rather than retrofitted with an AI plugin:
- Whole-codebase context. Rather than only seeing the file open in the active tab, the AI can be pointed at relevant files elsewhere in the project, so suggestions account for how a function is actually used, not just how it looks in isolation.
- Multi-file edits. A single instruction can produce coordinated changes across several files at once, rather than requiring a developer to manually propagate a change file by file.
- Fast iteration loops. Because the AI lives in the same window as the terminal and the diff viewer, accepting, rejecting, or adjusting a suggestion takes a keystroke rather than a context switch.
These are the same qualities that distinguish any modern AI agent from a simple autocomplete model — the difference is that Cursor delivers them from inside a familiar, general-purpose code editor rather than a dedicated agent interface.
Who Cursor Is Built For
Cursor tends to fit developers who want to stay hands-on with their code but want AI doing more of the mechanical work — writing boilerplate, proposing refactors, explaining unfamiliar parts of a codebase, or handling a well-defined feature end to end while still reviewing every diff before it’s committed. Teams that want a lighter-weight entry point into agentic coding, without fully handing tasks off to an autonomous agent, often start here before adopting more hands-off tools for specific workflows.
Tab Completion vs. a Standard Autocomplete Model
It’s worth distinguishing Cursor’s Tab feature from older code-completion tools, which were usually single-line predictions trained to guess the most statistically likely next token. Cursor’s completion model is still built on the same underlying transformer architecture behind modern language models generally, but it’s trained specifically on the editing process itself — not just what code looks like, but how developers actually move through a file: jumping to the next place that needs a matching change, predicting a multi-line edit rather than a single token, and anticipating a rename or refactor propagating elsewhere in the same file. That’s a meaningfully different training target than classic autocomplete, and it’s a large part of why Tab feels less like a suggestion engine and more like a collaborator finishing a thought already in progress.
Using Cursor on a Team
Individual developers are only part of the picture. Teams adopting Cursor typically care about more than any single feature: how consistently the AI follows a codebase’s existing conventions, how easy it is to review an agent’s multi-file changes before merging them, and how predictable the tool’s behavior is across different developers working in the same repository. Shared configuration files that describe a project’s conventions, allowed commands, and preferred patterns help keep agent-generated code consistent with what a human teammate would have written, which matters more as a team leans on agent mode for a larger share of day-to-day changes rather than just quick completions.
Limitations Worth Knowing
Cursor inherits the same general limitations as any AI coding tool built on large language models. Suggestions can be confidently wrong, particularly for unusual edge cases or very recent library changes the underlying model wasn’t trained on. Multi-file agent edits still benefit from careful review rather than blind acceptance, especially in codebases with non-obvious conventions a model can’t infer just by reading the code. And because switching models is one of Cursor’s selling points, output quality and style can shift noticeably depending on which model is handling a given request.
The Bottom Line
Cursor’s core bet is that AI coding tools work best when they live inside the editor developers already use, rather than asking them to adopt an entirely new interface. By combining fast inline completion, in-place edits, chat, and an increasingly capable agent mode — all model-agnostic — it occupies a distinct spot in the AI coding landscape: more integrated than a terminal-based agent, less autonomous than a fully hands-off engineering agent, and squarely aimed at developers who want AI as a close collaborator rather than a replacement.