Getting Started with Cursor and VSCode for AI Agent Development

The way developers write code is changing. Not through incremental improvements to IDEs, but through a fundamental shift: AI agents that understand your entire codebase and manipulate code across files in a single, coordinated operation.

The difference is more than just efficiency. When AI becomes a collaborator that works on multiple files simultaneously, understands your project’s architecture, and iterates until tests pass, it demands a different kind of workspace setup. A workspace designed around agents rather than individual files.

This article covers what you need to know to get started with AI agents in your development environment—whether you choose Cursor or VSCode with GitHub Copilot. You’ll learn the conceptual shift happening in code editors, how to decide between the two platforms, and what makes agent-focused development different from traditional AI-assisted coding.

Why Workspace Setup Matters for AI Agents

Traditional code editors treat AI assistance as an autocomplete layer—suggesting the next line based on context. Agent-based development is fundamentally different.

In traditional AI-assisted coding:

• The AI completes what you’re typing
• Context is limited to the file you’re editing, plus maybe nearby related files
• You guide every change through prompts and manual edits
• The AI is reactive—waiting for you to ask for help

In agent-based development:

• The AI takes on tasks and works until they’re complete
• Context spans your entire project—understanding relationships between files, frameworks, and patterns
• You set goals and review results rather than micromanaging edits
• The AI is proactive—asking clarifying questions and suggesting improvements before implementing

This shift creates practical demands on your workspace setup. Agents need broader context understanding to reason about architectural patterns across entire codebases, not just individual files. They require multi-file coordination to make changes across multiple interdependent files. They demand iteration and verification with clear signals—tests, type checking, linters—that tell the agent whether it’s on the right track. And they benefit enormously from project semantics where your workspace structure communicates intent through clear module boundaries, consistent naming patterns, and documented architectural decisions.

This is why the first step isn’t installing the tool. It’s understanding that agent-friendly workspace organization pays dividends the moment you start using them.

Cursor vs VSCode: Making the Choice

Before downloading anything, you need to understand the two fundamentally different approaches to AI-assisted development.

Cursor is a standalone IDE built on the VSCode foundation but reimagined around AI agents. Every feature is designed with AI-assisted development in mind.

VSCode with GitHub Copilot is the traditional approach: a lightweight, extensible editor enhanced with AI capabilities through extensions.

When to choose Cursor:

• You’re building projects where multi-file refactoring happens frequently (API changes, framework updates, pattern migrations)
• You want agents to take on larger chunks of work autonomously
• You’re willing to rely on a newer platform for state-of-the-art agent capabilities
• Your team wants parallel agent execution (multiple agents working on different features simultaneously)

When to choose VSCode + Copilot:

• You’re already deeply invested in the VSCode extension ecosystem
• You prefer familiar, proven tooling with mature enterprise support and compliance certifications
• Your current workflow is working well and you want to gradually layer in AI without changing editors
• You need maximum flexibility with different AI providers through custom extensions

Practical recommendation: If you’re starting fresh and plan to use agents heavily, Cursor’s integrated approach is worth the switch. If you’re already productive in VSCode and AI is additive to your workflow, Copilot extensions make sense. The good news: Cursor imports all your VSCode settings, extensions, themes, and keybindings, so switching is painless if you change your mind later.

Your First Setup: Creating a .cursorrules File

Download and install your chosen platform (Cursor from cursor.com or VSCode from code.visualstudio.com), then sign in with your GitHub account. Both are straightforward—the platform walks you through setup.

The key step happens after installation: configuring your workspace with rules that tell agents how to work with your codebase.

Create a .cursorrules file at your project root. This single file shapes every agent interaction with your project.

Here’s a production-ready template:

# Project Architecture

## Tech Stack
- Framework: Next.js 14 with TypeScript
- Runtime: Node.js 18+
- Package Manager: npm
- Database: PostgreSQL with Prisma ORM
- Testing: Jest + React Testing Library

## Build & Run Commands
- Development: `npm run dev` (starts on http://localhost:3000)
- Production build: `npm run build`
- Type checking: `npm run typecheck`
- Tests: `npm run test` (run specific file: `npm run test -- src/utils.test.ts`)
- Linting: `npm run lint`
- Database: `npx prisma migrate dev` (create migrations), `npx prisma studio` (browse data)

## Code Style & Patterns

### JavaScript/TypeScript
- Use ES modules (import/export), not CommonJS
- Destructure imports when possible: `import { foo } from 'bar'`
- Prefer functional components with React hooks over class components
- Use TypeScript strict mode; avoid `any` types

### Component Structure
- See `src/components/Button.tsx` for canonical component pattern
- Use `index.ts` exports for cleaner imports
- Co-locate tests next to components: `Component.test.tsx`
- PropTypes or TypeScript interfaces for all components

### API Routes
- All API routes go in `app/api/` following existing patterns
- Use consistent naming: `app/api/users/[id]/route.ts`
- Include error handling and status codes
- Write API tests in `__tests__/api/` directory

### Database
- Schema changes: edit `prisma/schema.prisma`, then generate migration
- Run migrations before commits: `npx prisma migrate deploy`
- Use descriptive column names; avoid abbreviations

## Workflows

### When Adding a New Feature
1. Write tests first (TDD approach): create test file in `__tests__/`
2. Implement functionality
3. Run `npm run typecheck && npm run test` before finishing
4. Ensure no linting errors: `npm run lint`

### Before Any Commit
- Run type checker: `npm run typecheck`
- Run full test suite: `npm run test`
- Run linter: `npm run lint`
- If any failures, fix before proceeding

### Code Review Checklist
- Tests added for new functionality
- No `any` types without explanation
- No console.log statements in production code
- Comments explain *why*, not what code does

Customize this template with your actual tech stack, frameworks, and coding standards. Save it to your project root. Both Cursor and VSCode agents will automatically read and follow it.

Why this approach works: Rules act as persistent context that shapes every agent interaction. Rather than repeating your tech stack and patterns in every prompt, you define them once. Agents automatically reference them, making every request more effective.

Understanding Agent Mode: How It Works Differently

Now that you understand the setup, what’s actually different about using agents?

Traditional AI code completion works reactively:

1. You type code
2. The AI suggests completions
3. You accept or reject each suggestion
4. The interaction is fast but interruption-heavy

Agent mode works proactively:

1. You describe a goal (“Add user authentication with JWT”)
2. The agent explores your codebase to understand patterns
3. It creates a detailed plan and asks for approval
4. It executes the plan, making changes across multiple files
5. It runs tests, encounters failures, and fixes issues autonomously
6. You review the final result once, not continuously

Key Agent Mode Features

Plan Mode (Cursor: Press Shift+Tab in agent input)

• Before writing code, the agent researches your project
• It creates a detailed implementation plan in Markdown
• You can edit the plan to redirect the approach
• Then the agent implements based on your approved plan

This is powerful because planning forces clarity. Agents plan better when they understand what they’re building. You can save plans to .cursor/plans/ for team documentation.

Chat with Codebase

• Ask questions about your project: “Where is the User class defined?”
• The agent searches your codebase and explains the answer
• Useful for onboarding to new projects
• Answers include file references and specific line numbers

Multi-File Editing (Cursor’s Composer)

• Request changes spanning many files: “Rename all setUser calls to createUser”
• Cursor’s agent makes coordinated edits across your project
• You review the diff and apply all changes at once
• Handles dependency tracking automatically

Streaming Changes in Real Time

• Watch the agent’s work as it happens
• Press Escape to interrupt and redirect mid-execution
• Much faster iteration than traditional code review

Creating an Agent-Friendly Workspace

Let’s walk through what a good agent-friendly project structure looks like.

File Organization

Agents reason better about codebases with clear semantic structure. Instead of generic folders like utils.ts and helpers.ts, organize by feature and intent:

src/
  auth/
    jwt.ts           # Token generation and validation
    login.ts         # Login flow
    logout.ts        # Logout flow
    middleware.ts    # Route protection
  users/
    schema.ts        # User type definitions
    handlers.ts      # Route handlers
    queries.ts       # Database queries
  components/
    ui/              # Reusable UI components
      Button.tsx
      Form.tsx
    pages/           # Page-level components
      HomePage.tsx
      ProfilePage.tsx
  __tests__/
    auth.test.ts
    users.test.ts
  config/
    constants.ts
    environment.ts

This tells agents what code is related. When they need to modify auth logic, they look in the auth/ folder. When dependencies span folders, the structure hints at relationships.

Documentation in Code

Add comments that explain intent, not just code:

// ARCHITECTURE: User authentication uses JWT stored in httpOnly cookies
// for XSS protection. Session validation happens on every protected route.
// Access tokens expire in 1 hour; refresh tokens in 7 days.
// See auth/jwt.ts for token generation and validation logic.

export async function validateSession(token: string) {
  // ...
}

Agents read comments and understand your architectural decisions.

Clear Data Structures and Contracts

Define clear data structures that agents can reference. This might be TypeScript interfaces, Python dataclasses, Go structs, Protocol Buffers, or documented schemas—whatever your language uses. The key is having a single source of truth:

# users/schema.py - Single source of truth for user shape
from dataclasses import dataclass
from datetime import datetime

@dataclass
class User:
    id: str
    email: str
    role: str  # 'admin' or 'user'
    created_at: datetime
    updated_at: datetime

When agents modify code that touches users, they reference this definition. Explicit data structures become a shared language between you and the agent, regardless of language.

Configuration Files

Agents work better when configuration is centralized and explicit:

{
  "name": "my-app",
  "version": "1.0.0",
  "engines": {
    "node": "18.0.0",
    "npm": "10.0.0"
  },
  "scripts": {
    "dev": "next dev",
    "build": "next build",
    "typecheck": "tsc --noEmit"
  }
}

This tells agents your environment constraints and available commands.

Key Terminology You Need to Know

Before diving into your first agent task, here are essential terms you’ll encounter:

Context Window

• The amount of text (measured in tokens) that an AI model can “see” at once
• Cursor uses 200k tokens in normal mode, with Max Mode extending to the underlying model’s maximum[1]
• VSCode with GitHub Copilot provides 64k-128k tokens depending on the model and VSCode version[2]
• Think of it as working memory: the larger the window, the more of your codebase the agent understands
• When working with large projects, agents must strategically select which files to include

Token

• A unit of text that AI models process; roughly 4 characters per token on average
• “Hello world” = approximately 3 tokens
• Awareness of tokens helps you understand why agents sometimes can’t see all your files simultaneously
• Staying within the context window is crucial for agent effectiveness

Worktree

• A Git feature that lets you have multiple branches checked out simultaneously without switching
• Cursor uses worktrees to run multiple agents in parallel without them overwriting each other’s work
• Each agent works in its own isolated copy of your codebase
• Worktrees are automatically created and cleaned up by Cursor

Agent Harness

• The system that orchestrates agents in Cursor/VSCode
• Combines three components: instructions (system prompts), tools (file editing, search, terminal), and user messages
• Different models respond differently, so the harness is optimized for each model
• Understanding this helps you write better prompts

Definition of Done

• Clear criteria for what successful completion looks like
• Agents need this explicitly: “All tests pass” or “No TypeScript errors” or “Type coverage > 95%”
• Without it, agents don’t know when to stop iterating
• Should be verifiable by automated tools (tests, type checking, linting)

Multi-File Edits (Composer in Cursor)

• Edits that span multiple files while maintaining consistency across them
• Harder than single-file edits because the agent must track dependencies
• This is where agents significantly outperform traditional AI assistants
• Enables large-scale refactoring that would take hours manually

What Happens Next: From Setup to Your First Agent Task

You now have:

• ✓ A configured IDE (Cursor or VSCode)
• ✓ A .cursorrules file with your project’s patterns and conventions
• ✓ A workspace organized for agent comprehension
• ✓ Understanding of key concepts

Before you start your first agent task, here’s what to do:

1. Open your project in Cursor or VSCode with the agents configured
2. Create or update your .cursorrules file with your actual tech stack and patterns
3. Run your build and tests to establish a baseline that works
4. Try a non-coding exploration task: Open the agent chat and ask something like:
   • “Summarize how authentication works in this project”
   • “What testing patterns are used in __tests__/?”
   • “Show me the User model definition”

See how the agent explores your codebase. This helps you understand how it thinks about your project before you ask it to make changes. Pay attention to which files it finds and how it understands relationships.

The next article in this series dives deeper into making agents effective: managing context windows strategically, understanding how agents find information in your codebase, and practical workflows for tasks like refactoring, testing, and debugging with agents.

Summary

AI agents demand different workspace thinking than traditional code editors. They need:

Broader context about your entire project, not just open files. Agent-friendly code organization multiplies their effectiveness.

Clear patterns and organization so they understand code relationships. Semantic folder structures, explicit data models, and documented architectural decisions become shared language.

Defined success criteria—tests, type checking, linters—so agents know when they’re done. These verification gates prevent incomplete or incorrect implementations.

A setup that enables iteration and learning. Agents improve through feedback. Your workspace should make it easy to run verification tools and understand results.

Cursor and VSCode + Copilot both deliver these capabilities, with different trade-offs. Cursor is more integrated and agent-first with superior multi-file coordination; VSCode is more flexible and extensible with proven enterprise support.[3] Both enable productive agent-driven development immediately.

The real productivity gains come in the next phase—when you learn to write prompts that agents understand, manage context strategically to keep agents focused, and orchestrate multiple agents on complex projects. But that starts with this foundation: a well-structured workspace, clear rules, and an IDE configured for collaborative development with AI.

Next article: Context Management and Multi-File Editing: Making Agents Effective — where you’ll learn how agents find information in your codebase, strategies for managing context windows on large projects, and practical techniques that multiply agent effectiveness.

Sources

[1] Cursor Docs, “Models” - https://cursor.com/docs/models
[2] GitHub Blog Changelog, “Copilot Chat now has a 64k context window with OpenAI GPT-4o” - https://github.blog/changelog/2024-12-06-copilot-chat-now-has-a-64k-context-window-with-openai-gpt-4o/; VSCode Docs, “AI language models in VS Code” - https://code.visualstudio.com/docs/copilot/customization/language-models
[3] GitHub Copilot official documentation - https://github.com/features/copilot