Cursor

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Cursor is an AI coding environment for understanding, creating, modifying, testing, and reviewing software. It combines an editor with AI agents that can search a repository, change files, execute commands, and inspect results. Its wider product includes terminal-based assistance, cloud execution, automated code review, and integrations with development tools.

Cursor supports both AI pair programming and delegated development. A developer can accept a small completion while typing, ask for an explanation of unfamiliar code, or assign an agent a larger implementation task. These workflows differ in how much work the agent performs and how much review the developer needs to provide.

Review date: November 2, 2026. Cursor’s interfaces, model catalog, subscription structure, and cloud capabilities change frequently. This article follows current documentation and distinguishes current offerings from older tutorials and legacy plans.


Cursor brings AI into the software-development workflow, helping people investigate, implement, and verify changes while leaving requirements, engineering judgment, and release decisions with the people responsible for the software.



Core Coding Capabilities

Cursor Agent

Cursor Agent combines instructions, tools, and a selected AI model to carry out development tasks. Its tools include file search, reading and editing, terminal execution, web access, and browser interaction. It can therefore investigate a problem, implement changes across several files, run checks, and use the resulting output to guide further work.

Agent also provides checkpoints for restoring file changes made during a session. These are useful when an implementation takes an unwanted direction, but they should complement normal version control. Restoring files should not be assumed to reverse external effects such as database changes, deployments, or actions taken through connected tools.

Tab Completion

Cursor Tab suggests code using surrounding code, recent edits, and information such as linter errors. Suggestions can span multiple lines, add imports, and anticipate related changes elsewhere in the project. The developer accepts, rejects, or partially accepts the proposed completion.

Tab is useful when a developer already understands the task and wants help expressing the next change. Agent is better suited to work that requires exploration or a sequence of operations. Both approaches still require checking that the result matches the intended behavior and project conventions.

Ask, Plan, Agent, and Debug Workflows

Cursor offers different modes for different stages of development. Selecting a mode establishes the kind of work the assistant should perform, such as explaining code, preparing an implementation, or investigating runtime behavior. A useful workflow may move through several modes as uncertainty decreases.

Mode Main purpose Typical use
Ask Read-only exploration and explanation. Understand a subsystem or locate the code responsible for a behavior.
Plan Investigate requirements and prepare a reviewable implementation plan. Design a feature that spans several files or systems.
Agent Implement changes using available development tools. Build features, refactor code, and run relevant checks.
Debug Investigate a defect using hypotheses and runtime evidence. Diagnose a reproducible failure whose cause is unclear.

Planning Before Implementation

Plan Mode researches the repository, asks clarifying questions, and produces a plan that can be reviewed and revised before implementation. It is particularly useful when several approaches are possible or the requested change affects multiple systems. The plan should identify the desired behavior, affected components, important constraints, and verification steps.

A plan is also an opportunity to detect missing requirements. For example, adding a document-upload page may require decisions about file access, size limits, retention, and failed uploads. Resolving those questions before generating the interface reduces the chance of producing a convincing demonstration with incomplete application behavior.

Debugging with Runtime Evidence

Debug Mode investigates defects by proposing explanations, adding diagnostic logging, and analyzing what happens when the problem is reproduced. The documented workflow asks the user to reproduce the issue, then uses the captured evidence to guide a targeted fix. Verification and removal of temporary instrumentation complete the process.

This approach is useful for timing problems, regressions, and failures that are difficult to understand by reading source code alone. A good report includes the expected result, observed result, reproduction steps, and relevant error messages. Diagnostic output should also be reviewed to ensure that temporary logging does not expose sensitive application data.

  • Debug Mode | Cursor Explains hypothesis-driven debugging, instrumentation, reproduction, and verification.

Codebase Understanding and Context

Cursor can combine exact searches with searches by meaning to find relevant code. Exact matching helps trace a known function, error message, or identifier, while semantic search helps when a user describes behavior without knowing where it is implemented. The agent can then follow references and read the surrounding code to build a more complete explanation.

This process is a form of context gathering, not proof that the model understands every part of the repository. Relevant information may be missing from documentation, hidden behind configuration, or supplied by an external service. Ask for file references and concrete evidence when an architectural explanation will guide an important change.

Useful requests include tracing a request from the interface to its database operation, locating existing validation patterns, or explaining how failed payments are handled. These questions help establish what already exists before the agent adds new code. They can also reveal reusable components that a broad generation request might overlook.

Browser Testing and Design Mode

Cursor Agent can operate a browser to inspect applications, interact with interface elements, and capture screenshots. This allows it to compare the running application with the requested change rather than relying only on source code. Browser checks are especially useful for layout problems, form behavior, and navigation flows.

Design Mode adds a visual way to request changes in the browser inside the Agents Window. Users can select elements, mark areas, or attach notes to the running page, and Cursor supplies that visual context to the agent. The agent then edits the underlying code.

Visual feedback makes it easier to communicate requirements such as aligning two components or adjusting the spacing around a particular control. It does not establish that accessibility, server-side validation, or authorization is correct. Those properties need their own checks alongside the visual review.

  • Design Mode | Cursor Documents visual annotations and directing code changes from a running application.

Rules, Skills, Plugins, and MCP

Rules and Project Instructions

Rules provide reusable instructions for how Cursor should work within a project or organization. Cursor documents project rules in .cursor/rules, user rules, team rules, and AGENTS.md instructions. Project rules use .mdc files and can be applied globally, by file pattern, manually, or according to relevance.

Good rules describe stable conventions, such as where API handlers belong, how the project validates inputs, or which commands verify a change. They should be short enough to remain useful and specific enough to resolve recurring decisions. Instructions guide the agent, but they do not replace enforced permissions, tests, or deployment controls.

  • Rules | Cursor Describes supported rule types, project files, scope, and application behavior.

Skills and Plugins

Agent Skills package specialized instructions, scripts, templates, and references in a reusable form. Cursor discovers available skills and can load their supporting material when relevant, while users can also invoke skills explicitly. This makes them useful for repeatable procedures such as preparing a release, reviewing a migration, or following an organization’s documentation process.

Plugins can distribute several customization components together, including rules, skills, subagents, MCP connections, and hooks. This is useful when a workflow requires both instructions and access to external tools. Review a plugin’s behavior and permissions before adopting it across a team.

MCP and External Tools

Model Context Protocol (MCP) connects Cursor to external tools and data sources. Depending on the server, this can provide access to documentation, issue trackers, databases, design systems, or operational APIs. Cursor documents support for tools, resources, prompts, and additional protocol capabilities.

An MCP connection can expand the agent’s authority as well as its information. A documentation search tool and a production database write tool have very different consequences even if both appear in the same conversation. Choose credentials and permissions that match the intended task, and distinguish reading information from changing external state.

Hooks

Hooks run scripts at defined points in the agent’s workflow. Cursor documents uses such as formatting edited files, collecting events, checking for sensitive information, and controlling certain tool operations. They provide a programmable mechanism for extending or constraining behavior.

A hook should be tested against both the allowed and disallowed cases it is meant to handle. An incorrect hook can interrupt useful work or fail to enforce the intended restriction. Treat these scripts as maintained development infrastructure rather than as instructions that need no further verification.

  • Hooks | Cursor Reference for lifecycle scripts and supported control points.

Subagents and Coordinated Projects

Subagents are assistants to which a parent agent delegates focused work. Each operates with its own conversation context and returns findings or completed work to the parent. Cursor supports this pattern across editor, CLI, and cloud workflows.

Separate context does not necessarily mean separate files. Cursor’s documentation notes that subagents can share a checkout unless isolation is requested, so concurrent edits may interfere with one another. Independent worktrees or cloud environments can separate changes until they are ready to integrate.

Projects provide a higher-level workflow for larger efforts. A coordinator plans and delegates work to coding agents, then brings the results back for review. This can help organize a migration or substantial feature, but integration checks remain necessary when independently produced changes meet.

Cloud Agents and Automations

Cloud Execution

Cloud Agents perform development work in remote environments rather than relying on the user’s local machine. They can implement changes, run tests, prepare pull requests, and produce browser-based evidence of application behavior. Their effectiveness depends on the configured repositories, dependencies, credentials, and network access.

Moving a conversation to the cloud does not automatically copy uncommitted local files. Cursor documents that the cloud agent starts from the remote repository’s Git state while receiving conversation context. Changes needed for the cloud task should therefore be made available in the appropriate remote branch before handoff.

Scheduled and Event-Driven Work

Automations run Cloud Agents on a schedule or in response to connected events. Documented triggers include source-control activity, Slack messages, Linear events, and webhooks. Examples include reviewing recent changes, triaging a reported defect, or preparing a periodic development summary.

An automation combines a trigger, instructions, tools, and any required repositories. It also needs clear boundaries for what it may change and where results should appear. Frequent triggers, broad repository access, and expensive model settings can increase both operating cost and the amount of work that requires review.

Code Review and Security Analysis

Bugbot

Bugbot reviews pull-request changes and reports suspected bugs, security problems, and code-quality issues. It can run automatically or be triggered manually, and its comments explain findings and suggest fixes. Current documentation describes integrations with several source-control services.

Bugbot’s output is an additional review input, not a guarantee that a change is correct. It may miss defects, report a concern that does not apply, or skip changes that exceed documented review limits. Reviewers should assess findings against the implementation and retain appropriate tests and human review.

  • Bugbot | Cursor Documents pull-request analysis, repository integrations, triggers, and limits.

Security Agents

Cursor documents two dedicated security workflows: Security Reviewer examines pull requests before merge, while Vulnerability Scanner examines the existing codebase. Both run through the Automations platform and require Cloud Agents. Their different scopes help distinguish newly introduced issues from weaknesses already present in a repository.

Security findings still require investigation and prioritization. Whether a reported issue is exploitable can depend on deployment configuration, data flow, and access controls outside the changed code. Automated analysis should complement the organization’s broader security process.

CLI, SDKs, and Related Products

Cursor CLI provides agent assistance from the terminal, including interactive conversations and non-interactive execution for scripts or continuous-integration workflows. Its documented modes include Agent, Plan, and Ask. It also supports resuming sessions and handing work to Cloud Agents.

Cursor publishes TypeScript and Python SDK documentation for programmatic integrations. These interfaces are relevant when a team wants to incorporate agent operations into its own development tools. Check the current API reference, permissions, and supported execution model before designing an unattended integration.

Origin and Grok Bot

Origin is Cursor’s repository-hosting service, documented as an early beta at the review date. It supports Git operations, repository browsing, GitHub mirroring, and pull-request workflows. Hosting source code is distinct from hosting the production application built from that code.

Grok Bot is documented as a persistent assistant working on a cloud computer with browser, filesystem, and terminal access. It extends the product’s scope beyond a single coding session toward ongoing tasks in connected tools. Its permissions and persistent context should be evaluated separately from a short-lived local editing task.

Cursor and No-Code Development

Cursor allows people to request software changes in natural language, which lowers the amount of code they must write manually. Its primary working material remains a software project with source files, dependencies, tests, and deployment configuration. This makes it relevant to no-code experimentation while preserving many of the responsibilities of conventional development.

For a prototype, a user might describe a dashboard and iteratively refine it through Agent and Design Mode. A production application also needs decisions about authentication, data storage, hosting, backups, monitoring, and operating costs. The coding assistant can help implement those decisions, but the existence of generated code does not establish that the complete service is ready.

Task How Cursor can help What remains to verify
Build a prototype Generate and revise an initial implementation. Whether the main workflow works beyond demonstration data.
Extend an existing application Find relevant code and modify connected components. Compatibility with existing behavior and architecture.
Build an AI feature Implement model calls, retrieval, and interfaces. Data permissions, output quality, latency, and cost.
Deploy software Prepare configuration and operate available deployment tools. Credentials, target environment, approvals, and recovery.
Maintain a project Investigate defects, review changes, and automate recurring work. Correctness, regressions, and ownership of the resulting changes.

Practical Example: Adding Document Search

The following is an illustrative development workflow, not a claim that a single prompt produces a production-ready system. Suppose an existing customer portal needs a search feature that answers questions using approved documents. The work combines repository exploration, access control, retrieval, interface development, and evaluation.

  1. Explore the current application. Locate authentication, document storage, authorization checks, and existing tests.
  2. Prepare a plan. Specify permitted document types, indexing behavior, retrieval filters, and answer presentation.
  3. Implement a small complete workflow. Start with one authorized user searching one approved document collection.
  4. Add source references. Show the documents or passages supporting an answer.
  5. Handle uncertainty. Return an explicit insufficient-information response when retrieval does not support an answer.
  6. Test access boundaries. Confirm that one customer cannot retrieve another customer’s documents.
  7. Review the interface. Check loading, empty, failure, and mobile states in the browser.
  8. Evaluate quality and cost. Use representative questions and monitor retrieval errors, latency, and model usage.
  9. Review and release. Inspect the diff, run relevant checks, and follow the project’s deployment process.

Example Planning Prompt

Inspect this repository and plan a document-search feature for the existing customer portal. Reuse the current authentication and authorization mechanisms. Customers must only search documents they are allowed to read. Identify the files and services that need to change, how documents will be indexed, and how answers will cite their sources. Include tests for cross-customer access, missing documents, unsupported answers, and unavailable AI services. Explain any assumptions or missing requirements before implementation.

A follow-up implementation request should refer to the reviewed plan and define the expected evidence of completion. For example, ask for a working search flow, the relevant test results, and an explanation of any remaining deployment configuration. This connects generated code to observable behavior rather than judging progress by the volume of changes.

Privacy, Permissions, and Data Handling

Privacy Mode concerns how customer data is used and retained; it does not mean that AI processing occurs entirely on the user’s computer. Cursor’s data-use documentation states that enabled Privacy Mode prevents training on customer data and describes provider retention arrangements. It also identifies exceptions involving abuse investigations and models that require separate retention arrangements or administrative opt-in.

Cloud execution introduces additional storage needs because agents require code and environment data while working. Teams should review the applicable retention settings and distinguish model-provider handling from Cursor’s storage of conversations, artifacts, and execution environments. The appropriate configuration depends on the project’s data and the organization’s requirements.

Ignore Files Are Not a Complete Security Boundary

A .cursorignore file controls which files are available through specified Cursor context and file-access mechanisms. However, Cursor explicitly documents that terminal commands and MCP tools operate outside those ignore-file restrictions. Excluding a file from ordinary AI context therefore does not guarantee that every available tool is unable to access it.

Use credential permissions, execution controls, and appropriately scoped environments for actual access restrictions. Repository documents, web pages, and tool responses may also contain misleading instructions, making prompt injection relevant to agent workflows. Review external actions according to their effects, especially when an assistant can modify production systems.


Evaluation and Limitations

Cursor can reduce the effort required to explore a repository and produce an implementation, but generated changes can still contain incorrect assumptions. Typical problems include invented APIs, missed access checks, unnecessary rewrites, and tests that repeat the implementation without testing the requirement. Evaluate the resulting software through its behavior and evidence.

The cost of AI-assisted work also includes review and correction. A useful assessment compares complete task outcomes, such as a verified defect fix or accepted feature, rather than counting generated lines of code. Track regressions, review time, operating cost, and maintainability alongside development speed.

Evaluation area Useful question
Correctness Does the implementation satisfy the actual requirement?
Context Did the agent inspect the relevant code and configuration?
Verification Do the checks exercise meaningful behavior and failure cases?
Security Are permissions and data boundaries preserved?
Maintainability Does the change fit the project’s architecture and conventions?
Cost Does the completed workflow justify its model and review costs?
Release readiness Are deployment configuration and recovery steps understood?

Learning Resources

Additional Viewing

Load video
YouTube

Riley Brown’s Cursor 2.0 Tutorial for Beginners, featuring Kehan Zhang, demonstrates project creation, application development, debugging, multiple agents, and deployment. The November 2025 recording provides an extended example of iterative development, including planning and full-stack implementation. Its interface and product details predate the current documentation, so use it for workflow demonstrations rather than current pricing or exact configuration instructions.

Related PRIMO.ai Resources

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