AI IDEs: Choose an Editor for Your Development Workflow
AI-enabled editors differ in how deeply they can read your codebase, what changes they can make autonomously and how they handle project instructions. Here is how to compare them for your workflow.
Updated 5 min read
An AI-enabled IDE pairs a code editor with an AI assistant that can read your repository, propose edits and run commands on your behalf. The editors differ in how much of your codebase the AI can see at once, how much it can change without your confirmation, and where it stores your project instructions.
Tools like fenbs keep requirements and context outside any one IDE, so the work record travels with you when you change editors or coding assistants.
How AI IDEs differ from traditional editors with plugins
A traditional editor with an AI plugin, such as VS Code with a Copilot extension, adds suggestions and a chat panel to an existing workflow. An AI-first IDE is built around the assistant from the start. The distinction matters in three areas: context, control and instructions.
- Context: how much of the repository the assistant can read in one request. A small context means the assistant misses references in files it has not read.
- Control: whether the assistant can edit multiple files, run terminal commands and apply changes automatically, or only suggest them.
- Instructions: whether you can give the assistant standing project rules, and where those rules live.
Editors to compare
The main AI-enabled editors as of late 2026 are Cursor, VS Code with GitHub Copilot, and JetBrains IDEs with AI Assistant or Copilot. Devin Desktop (formerly Windsurf) is an agent-first environment that takes over longer multi-step tasks rather than assisting inline. Each has a different balance of context depth, edit scope and project instructions.
| Editor | Context approach | Edit scope | Project instructions |
|---|---|---|---|
| Cursor | Full repo indexing with semantic search; reads files on request. | Can edit multiple files and run terminal commands in agent mode. | AGENTS.md or rules files placed in the repo; also supports inline instructions. |
| VS Code + GitHub Copilot | Open files and workspace index; smaller context than Cursor by default. | Inline suggestions, multi-file edits in Copilot cloud agent; confirm before apply. | Instructions in .github/copilot-instructions.md or inline in chat. |
| JetBrains + AI Assistant | Open files and project structure; reads referenced code on request. | Inline completions and code actions; multi-file edits in limited scope. | Inline chat context; no dedicated project instructions file by default. |
| Devin Desktop | Clones the repo and reads it end to end for each task. | Autonomous multi-step changes; intended for longer scoped tasks, not inline editing. | Task description and attached files; reads AGENTS.md in the repo. |
| fenbs | Keeps requirements, decisions, and context notes outside any IDE. | Not an editor. Connected AI assistants read and update the work record over MCP. | Decisions and rules in fenbs are read by every connected AI before it acts. |
Check the vendor's current documentation before deciding. Context limits, agent modes and instruction formats change with product updates. The table above reflects behavior confirmed on vendor pages in late 2026.
Keep project requirements outside the IDE
Switching IDEs or AI assistants loses any context stored only inside a chat. fenbs keeps your project requirements, decisions and tasks in one place that any connected AI reads over MCP at the start of a session. Create a free account, add your project, and connect your IDE's AI over the Integrations page.
Create a free accountFree for one person · No credit card · Works with Claude, ChatGPT, Cursor and more
What makes a good set of project instructions
Project instructions are rules the AI reads before every session. Useful instructions describe decisions that are not obvious from the code: why a particular approach was chosen, what the team has agreed not to do, and what a reviewer needs to see before a change ships.
Keep instructions short and specific. A list of ten firm rules is more useful than three pages of background. If a rule no longer applies, remove it rather than leaving it to confuse a later session. How to use AI at work covers keeping shared instructions accurate across a team.
Choosing between an inline assistant and an agent mode
An inline assistant suggests one change at a time and waits for you to accept or reject each one. An agent mode takes a task description, plans a sequence of changes and applies them to multiple files. Both approaches have their place.
Use inline suggestions for small, targeted edits where you want to review each change immediately. Use agent mode for well-scoped tasks where you have written clear requirements and can review a diff before applying. Never use agent mode for a task whose scope you have not defined, because the assistant will define scope itself.
How context travels when you switch editors
Each editor stores its chat history and any in-app memory privately. When you switch from Cursor to VS Code, or from one machine to another, that history stays behind. The code itself moves with the repository, but the reasoning behind recent decisions does not.
Keeping requirements and decisions in fenbs means the next session, in any editor, starts with the same agreed facts. The assistant reads the project context over MCP, then works inside the editor. What is an AI brain explains the principle behind this separation.
Skills and prompts you use repeatedly
Some prompts are worth reusing: the instruction you give before a code review, the format you want for commit messages, the steps for adding a new API endpoint. Most IDEs let you save prompt templates, but those live in one editor on one machine.
In fenbs, Skills and prompts are stored in the project and available to any connected AI. A team member who sets up a different editor for the same project can still read the same prompts rather than reconstructing them from memory.
How to do this with fenbs
- Create a free account and add your current project.
- Write your project's key requirements and decisions in fenbs: what the project is for, what the team has agreed not to do, and the current tasks.
- Connect your IDE's AI assistant over MCP from Integrations. Cursor, Claude Code, GitHub Copilot and others are supported.
- Ask the AI to read the project context at the start of each session before it edits code.
- Before you change IDEs or coding assistants, ask the current assistant to save progress, including where each task stands and the next step.