I’ve been experimenting with the idea of running multiple coding agents in parallel, and the tooling around it seems almost as important as the agents themselves.
Once you have 3–5 agents working on different tasks, you need more than separate terminals:
Isolated workspaces or worktrees
Clear task assignment
Shared project context
Agent status and progress
Easy review of each agent’s changes
A way to manage several agents without losing track
Tools like Claude Code, Codex, Cursor, and other coding agents can handle the actual work, but I’m curious about the layer that coordinates them.
I’ve been looking at Sharkly.ai, a free platform that lets you manage multiple AI agents and human teammates in the same project workflow.
For developers here, what are you currently using to run multiple coding agents in parallel?
I’ve ended up treating the coordination layer as a separate system from the coding agents themselves.
The coding agent can be Cursor, Codex, Claude Code, etc. What mattered more once I started running several jobs in parallel was putting a control plane around them.
The pattern I’ve settled on is roughly:
each job gets a bounded task definition and unique ID
every run gets an isolated Git worktree/branch
jobs move through explicit queued/running/verification/checkpoint states
the coordinator tracks which execution slots are occupied and prevents conflicting work from running together
completion produces a specific checkpoint plus verification evidence rather than simply trusting the agent’s “done”
merge/deployment are separate authorization steps
failed, stale, or ambiguous state stops the job rather than letting the agents improvise
a persistent status view keeps recently completed work visible so parallel jobs don’t disappear from operator awareness
One lesson has been that I don’t really want the agents coordinating one another. I want them relatively disposable and independent, with a deterministic layer responsible for assignment, isolation, state, verification, and review.
That has made running several coding agents at once much more manageable than simply opening more terminals. -SS
Strong agreement on the core point, and I’d push it further: the deterministic layer is the product and the agents are interchangeable. Once you accept that, a lot of the design questions answer themselves.
The line I’d underline in your list is “completion produces a specific checkpoint plus verification evidence rather than simply trusting the agent’s done.” That’s the step most setups skip and it’s where the failures actually come from. Agents reporting success on code that doesn’t compile is the single most common thing I’ve had to design around.
One thing I’d add to it: run verification in a process that shares no context with the agent that did the work. If the same model that wrote the code also judges whether it’s correct, it grades its own homework and the failures correlate, so it’ll be confidently wrong in exactly the places it was already confidently wrong. A fresh context given only the task definition and the diff catches noticeably more.
On isolation, worktrees are the right call but they isolate the repo and nothing else. The collisions that have bitten me were all shared state outside it: a dev server port, a database, a cache directory, a lockfile in the home directory. Two agents running tests in separate worktrees still fight over localhost:3000.
Disclosure: I build Grunz, which is a coding agent, so I’m on the agent side of this rather than the orchestration side. https://grunzai.com if it’s useful to you. But honestly the control plane you’ve described is the harder and more interesting half of the problem, and I’d read a longer writeup of it.