Choosing an IDE for agent orchestration: what you need to run many AI agents

The right IDE for agent orchestration depends on how many agents you run and for how long. For pairing on code, an editor with agent tabs is hard to beat. For many agents working over days, you need one view of all work, owners, schedules, a single approvals inbox, logs, deliverables, and runs that continue with your editor closed.

What do you need from a tool to orchestrate AI agents?

Write down what you want to know about your agents on a busy Thursday afternoon, and the requirements fall out of that list:

• One view of all work. Everything running, scheduled, waiting and done, across every agent, on one screen.
• Ownership. Each task has one agent responsible for it, so you know who to ask.
• Schedules and triggers. Work that runs every Monday, or when a webhook fires, without you starting it.
• Approvals in one place. Every "I need you" from every agent, in a single queue you can clear from your phone.
• Logs you can find. What happened in a run, retrievable next week without reopening a session.
• Deliverables. The files, links and reports each task produced, kept next to the task.
• Runs that outlive your session. Agents keep working when the editor, the terminal and the laptop are closed.

Score your current setup against this list before you shop for agent orchestration tools. Whatever you use today probably covers a few of these well, and you're doing the rest by hand, usually by keeping it in your head.

What is an IDE good at when you work with AI agents?

Pairing on code. When you sit with one agent on one repository, an editor gives you the tightest loop available: the agent sees your open files and proposes a change, you read the diff inline, run the tests and push back, all in the same window. For getting from an idea to a reviewed change, that loop is hard to improve on.

Agent tabs and side panels stretch the same loop to a few parallel sessions. One agent refactors while another writes tests, and you flip between them, reading each diff as it lands. For a focused afternoon inside a codebase, this is the right tool and you should keep using it.

The fit rests on two assumptions: you're present, and the work is code in that repository. Both hold during a pairing session. Neither holds for a weekly competitor scan, a launch post, a nightly dependency update or a six-hour job that runs while you sleep, and those are exactly the jobs that pile up once you run several agents.

Where does an editor-based setup run out for many agents?

It runs out at roughly the point you stop watching. These are the common breaks:

• Runs end when the laptop sleeps. A local agent session lives and dies with your machine.
• Tabs make a poor schedule. An editor is built around the session you're in, so there's rarely a place to say "run this every Monday at 8:00" or "start when a payment fails."
• Approvals hide in tabs. Each agent asks for permission in its own panel, so you find out an hour later that one has been waiting.
• Non-code work has no home. Posting, email, research and outreach don't belong to a repository.
• History is scrollback. Finding what an agent did last Tuesday means reopening sessions and scrolling.
• You become the router. With five tabs open, you decide who does what, carry context between them and remember what's done.

The editor stays a great workstation through all of this. Running a team of agents for days needs a control surface on top of it, and we cover the day-to-day friction in more depth in managing many agent sessions.

Can terminal multiplexing manage many AI agents?

Up to a point, and plenty of engineers run their agents this way for good reasons. You get one pane per agent, each in its own git worktree, on a remote machine so sessions survive your laptop closing. It's cheap, scriptable and entirely under your control.

The costs show up as the number of agents grows:

• Visibility means cycling panes. You check on agents by looking at each one in turn.
• Approvals are whichever pane is waiting. A prompt waiting for you looks a lot like an agent that's busy.
• Schedules are cron jobs you write. Triggers, retries and time zones become your code to maintain.
• Deliverables are wherever the agent left them. A file in a worktree, a link somewhere in scrollback.
• Credentials get copied around. Each pane needs its own keys and logins.

If you run a few agents on code and enjoy building your own tooling, this works well. Past that, you're maintaining a homemade orchestration system, and your evenings go into the glue.

What does a calendar workspace add for agent orchestration?

A calendar workspace treats agent work as tasks on a timeline, so the basic questions (what's running, who owns it, what's waiting on me) have answers you can see. Each task has an owner, a status and a date. Routines appear on the days they run, events appear when they fire, and multi-day work shows as one band across the days it takes. You scan a week of agent work the way you'd scan a team calendar.

Approvals from every agent collect in one inbox, so nothing waits unseen in a tab. Deliverables attach to the task that produced them, which puts last week's output one click away. And because the agents run in cloud environments, the work keeps moving while your editor, terminal and laptop are closed.

The trade-off is distance from the code. A calendar is a poor place to pair on a tricky function line by line, so if you write code, you'll keep your editor for that and use the workspace for everything that runs without you.

How do editors, terminals and workspaces compare for agent orchestration?

Here's how the three mechanisms score against the requirements:

Read it row by row against your own work. If your agents mostly write code in sessions you're watching, the first column covers you. If several of the rows you care about point at the third column (scheduled jobs, approvals from agents you aren't watching, work outside a repository), you've outgrown tabs and panes, whatever the tool calls itself.

How do you choose the right setup for your agents?

List the agent jobs you ran last month, then ask three questions of each one: is it code in one repository, do I watch it run, and does it happen only once?

• Yes to all three: keep it in your editor. Pairing is what the editor does best.
• Code, but long or parallel: give each agent an isolated environment on a machine that stays on, plus one place to review diffs when they finish.
• Recurring, triggered, or not code at all: this needs an orchestration surface with schedules, an approvals inbox and deliverables on the task.

If you run several agents, your list will probably split across all three. The pattern that holds up is to pair in the editor while you're at your desk, hand everything recurring, long-running or non-code to a workspace where one manager agent plans it and specialist workers run it, and then use the editor again to review what they shipped. If you're designing that hand-off yourself, the stages of an agent orchestration pipeline walks through each one.

How does Kavela's calendar workspace handle agent orchestration?

Kavela is a cloud workspace for AI agents, laid out as a calendar. You talk to one agent, Kav, who plans the work, files tasks and hands each part to the right worker agent.

• One view: every task, routine and event lands on a month, week or day calendar, with multi-day work as one band.
• Ownership: Kav hands each task to one worker agent, so every task has an owner.
• Triggers: routines (every Monday at 8:00, in your time zone) and events (a webhook, a file added to the workspace).
• Approvals: sign-ins, choices, posts and deploys wait in one Approvals inbox ("N need you"), also on Telegram. Everything else just runs.
• Deliverables: files, preview links and reports attach to each task, plus a workspace-wide Delivered list.
• Always on: each worker has its own cloud computer with a shell, files, git and a browser, so runs keep going after you close the tab or your laptop.

You keep your editor and your subscription. Choose "My Claude plan" or "My Codex plan" per agent, and Claude Code or Codex runs in that agent's cloud computer with the workspace's files, skills and connected apps.

Kavela is in early access and runs on your own Claude or Codex subscription, with a 14-day free trial. Start your free trial