An ADE (Agent Development Environment) is a development environment designed to organize the work of multiple coding agents. It brings their terminals and workspaces together so you can assign tasks and track changes without getting lost between windows. It makes sense when you work with agents in parallel and need to keep track of what each one is doing in your repository. (onorca.dev)
The shift is in what you oversee. While an editor organizes your work around files, an ADE puts tasks and the agents running them front and center. You still decide what needs to be built and which changes to accept.
What’s the difference between an IDE and an ADE?
An IDE brings together the tools you need to write, run and review code. You can add agents, open terminals and work across multiple branches, but it’s up to you to keep the connection between each task, its process and its files clear.
Source: Orca — The agent development environment
An ADE puts that organization at the heart of the interface. In Orca, for example, worktrees, terminals and agents are all part of the same workspace. You can track multiple tasks in the repository without having to piece together what’s happening in each tab. (onorca.dev)
Think of it as a workbench. In an IDE, your tools are laid out so you can work on the code. In an ADE, you set up several workbenches, assign a task to each one and review what they produce.
That doesn’t mean you have to abandon your editor, either. The ADE helps you coordinate the work, while reading an implementation, understanding a dependency or reviewing an architectural decision is still your responsibility.
How worktrees keep each agent’s work separate
Git worktrees are central to this model. Each has a working directory with its own files and branch, linked to the same repository. Orca uses that separation to connect tasks with their terminals and agents. (onorca.dev)
Say you want to fix a form and add a filter to the product listing. You assign the form to one agent and the filter to another, each in its own worktree. They work in separate directories, so their edits don’t get mixed together as they carry out their tasks.
That separation also helps you compare solutions. You can give the same task to different agents, let each work in its own space and then review which proposal fits best. Orca supports this parallel-agent workflow. (onorca.dev)
But file isolation has a limit: you still have to integrate the changes afterward. If two tasks change how the same feature behaves, you’ll need to check how they fit together. Separate directories stop agents from writing to the same working files; reviewing the combined solution still needs to happen.
That’s why it’s worth separating tasks by scope. A specific fix and an independent feature are easier to review than two agents reorganizing the same part of the application at once.
What Orca offers as an example of an ADE
Orca is an application that brings terminal-based agents and worktrees together in one interface. It integrates tools such as Claude Code, Codex, Gemini CLI and OpenCode, among others, and lets you organize their sessions alongside the project’s files and terminals. (onorca.dev)
The model writing the code is still the one used by whichever agent you connect. Orca provides the organization: which task is open, where each agent is working and which changes you need to review. Your AI subscriptions or credentials are still part of the setup.
That distinction affects the cost. A free environment doesn’t make the agents free to use. If you launch several tasks at once, you’ll need to monitor each tool’s usage just as you would when running it directly from the terminal.
An ADE shouldn’t be confused with a role-based system, either. Organizing parallel sessions and deciding that one agent should code while another verifies are related but separate decisions. I cover the split between implementation and review in MiniMax Code and the leap from using one agent to building a team.
How to organize a task with multiple agents
Start with a task you can review independently. Opening agents without defining what each one should deliver just spreads the confusion across more terminals.
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Define the scope of each change. Separate the form fix from the new filter. Specify the behavior you expect and which parts of the project should stay outside the task’s scope.
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Assign a worktree to each task. Check that each agent is working in the right space before letting it modify files.
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Give it context about the project. Include the instructions it needs to follow the repository’s structure and the checks it should run. If you’ve already documented those rules, reuse them.
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Track the status of each session. Keep tabs on which agent is working, which is waiting for an answer and which has finished. Clear up any questions before it continues on a wrong assumption.
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Review each proposal separately. Read the changes, check the behavior and decide what to accept before integrating it with the rest.
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Check the combined result. Once you’ve incorporated the changes, run the project’s checks and make sure the features still work together.
Shared instructions reduce the need to explain the repository from scratch in every session. The article on how to use AGENTS.md to guide your AI when coding goes into that part of the work.
If several of you work on the same project, also agree on who reviews and who integrates the changes. An organized dashboard helps you track sessions, but the team needs to define the acceptance criteria.
When an ADE is worth using and when to stick with your editor
An ADE is a good fit when your main challenge is coordinating multiple tasks. If you switch between agents, keep several changes in progress or need to compare implementations, grouping each task with its workspace cuts down on manual organization.
It also makes sense when you repeat that workflow often. Setting up worktrees and opening terminals manually is a valid option; a dedicated interface groups those operations together and keeps their connection to the project visible.
If you work with a single agent and tackle small tasks one after another, your editor and a terminal may be enough. Switching tools adds little if you barely have any sessions to coordinate.
The most important factor is your capacity to review. If you’re already building up a backlog of changes to read, opening more agents adds to that queue. Before running another task in parallel, finish checking the proposals in front of you.
There’s another practical consideration: how much context the changes share. The more they depend on one another, the more attention you’ll need to give their integration. To start experimenting, choose small tasks with clear boundaries.
Limits: resources, usage and code review
Each active agent adds processes and activity to the environment. If you’re also running the application, its tests and other services, you’ll need to keep an eye on your machine’s RAM and CPU. When opening sessions is easy, you can end up keeping more work in progress than your computer can comfortably handle.
AI usage is also worth checking before launching parallel tasks. Comparing agents means having several of them try to solve the same problem. Do it when you want to evaluate their proposals and can set aside time to read them.
Be careful with code review: a task marked as complete still needs your judgment. Check that it solves the problem you asked it to solve, follows the project’s structure and doesn’t introduce unrelated changes. In products you’ll be maintaining, accepting an implementation that’s hard to understand leaves you with more work later.
A review agent can help you spot issues, but you decide what goes into the repository. Separating implementation from verification is useful when the second agent gets specific criteria for checking the result.
Open a small project in Orca and assign independent tasks to separate worktrees. Review each change before integrating it, and only increase the amount of parallel work when you can keep track of it without building up a backlog of proposals.
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