AI Team OS – Turn Claude Code into a Self-Managing AI Team

Johnny_Bonk

Interesting, what are the benefits and drawbacks you've found developing and using it yourself?

rsolva

Nifty, looks like the enterprise edition of OpenClaw, kinda. Also, it looks token hungry!

soared

Does anyone have an example of input, output, and cost?

wpasc

I see some tools like this that keep popping up (don't mean that in a bad way! it's clearly exciting and the README itself compares itself to similar tools). however, for coordination strategies like this, aren't you always having to use token-based pricing via some API Key? that's the largest think that holds me personally back from getting into something like these frameworks. With a claude code max plan, all my delegation and coordination has to be done within a session (between some agents) with persisted artifacts. Unless I'm missing something that has changed? Perhaps it's all moot as the usage you get from a subscription plan will eventually no longer be subsidized. Also, I have to wonder about what layers of coordination done externally to a model can be persistently better than within tool coordination? Like, with an anthropic feature like agent teams, I feel like it might be tough to beat anthropic native coordination of various Claude sessions because they might have better internal tool and standards awareness, which makes feeling like plugging something like this more difficult unless one's goal is to plug something like this into an open source model. Geniunely curious how other people are thinking about this!! Edit: I actually see that this tool claims that it can run within your existing Claude Code subscription, so now I'm extra interested.

heyitsaamir

Sort of feels like gastown enterprise edition

Matticus_Rex

But does it work? and well?

cronus1141

I want to hear more feedback and ideas from anyone interested. Here's why I built this: I've been using Claude Code daily for a while. Tried running multiple projects simultaneously — and quickly hit the wall where I was the bottleneck, not the AI. Every step needed me: correcting course, enforcing constraints, switching between projects to tell CC what to do next. "Go research this and write me a report." "Design a better architecture for this module." "Here's what happened last session, pick up where we left off." Over and over. What I actually wanted: to hand off everything the AI can handle independently, and only get pulled in for decisions that affect project direction. Record those strategic questions, pause that thread, let me review when I have time. But don't stop working — pick up the next task, do the research, run the tests, organize your findings. What pushed me over the edge was using Lobster (the browser automation agent). I had it register social media accounts, scrape topic data, analyze users, and publish content. It technically worked — but it took 2 hours, burned $12 in API costs, and I had to sit there the entire time giving feedback and corrections. The end result was fine, but the process was basically me babysitting an expensive intern. That's when the economics clicked. I'm on the $200/month CC Max plan. Unless you're running high-intensity work non-stop, it's hard to burn through the allocation. The tokens are pre-paid. So the question flips: it's not "how do I minimize token usage" — it's "how do I maximize the value from tokens I'm already paying for?" If you can ensure the burned tokens produce real output, then the only questions are how fast can you burn and how much value comes out. From what I've seen, CC with sufficient permissions just does more per dollar than API-based agents. The answer was obvious: make the AI work more so I work less. So I built this to be the lazy CEO's toolkit. I set the rules, design the structure, make the strategic calls. Everything else — task management, agent coordination, research, testing, meeting facilitation — goes to the AI. When it needs my input, I want it to come with a summary that's already been through multiple rounds of research and synthesized different perspectives. Not "what should I do next?" but "here's what we found, here are the tradeoffs, what's your call?" It's not there yet. The Leader gets overloaded, soft rules get ignored sometimes, meetings need more refinement. But the direction is clear: reduce human interrupt-driven management to strategic decision-making only. If you've hit similar friction with CC or other coding agents — what's the thing you most wish you could stop doing manually?

jerrygoyal

The bottleneck is quality. Underlying AI models aren't good enough for fully autonomous systems. Every task I assign to Claude, I have to review and steer it in a certain direction. Until underlying models get better, all these "teams of AI coworkers" will not work.