@gkxspace: This is what an AI-native team should really look like! I spent three weeks building an AI team with OpenClaw, having multiple agents collaborate in Discord—it was a total waste of time (and I don't think I'm alone). The problems I encountered: tokens burning like crazy, agents 'fighting' each other...

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Summary

The author shares the issues they encountered when building a multi-agent team with OpenClaw, such as token waste and infinite loops, and found that helioim_ai achieves more efficient human-AI collaboration by giving each AI an independent identity and clear boundaries of responsibility, along with a nighttime 'dreaming' self-improvement mechanism.

This is what an AI-native team should really look like! I spent three weeks building an AI team with OpenClaw, having multiple agents collaborate in Discord—it was a total waste of time (and I don't think I'm alone). The problems I encountered: tokens burning like crazy, agents 'fighting' each other, two AIs keep @-ing each other endlessly, all my money wasted... I tried all sorts of prompt constraints, adding an intermediary judge, limiting rounds—no matter how I tuned it, it didn't work. Sometimes collaboration was too rigid, sometimes as soon as I loosened permissions, it went out of control. So when I saw @helioim_ai having multiple AIs work in channels, my first reaction was disbelief, and then I went and tried it... But after actually trying it, I realized I had underestimated it. It's not about 'having agents send prompts to each other,' but giving each AI an independent identity (with a name, email, and clear scope of responsibilities). For example, the product manager only breaks down requirements, the designer only creates solutions—each does their own work and then synchronizes in the channel. It's not endless conversations, but division of labor and collaboration. There's also a mechanism that surprised me: every day in the early morning, the AI 'dreams,' reviewing the day's work, figuring out what went right and wrong, and updating its own code of conduct. Looking back, the pitfalls I encountered with OpenClaw (token waste and agent infinite loops) were essentially because I treated 'multi-agent collaboration' as a prompt chain orchestration problem. Instead, they directly give AIs identity and responsibility boundaries, making collaboration happen like human teamwork. So far, this approach seems right.
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Cached at: 05/26/26, 03:12 PM

This is the ultimate form of an AI-native team!

I spent three weeks building an AI team with OpenClaw, letting multiple agents collaborate in Discord — it was a complete waste of time (and I’m pretty sure I’m not the only one).

The problems I ran into with my own setup: Tokens burned through like crazy, agents “fighting” each other, two AIs endlessly mentioning each other back and forth — all my money wasted…

I tried various prompt constraints, added a middle judge, limited rounds — nothing worked. Sometimes the collaboration was too rigid and brittle, other times giving them more freedom led to total chaos.

So when I saw @helioim_ai having multiple AIs work in channels, my first reaction was disbelief. Then I tried it…

But after actually testing it, I realized I had underestimated it. It’s not about “agents sending prompts to each other” — it gives each AI an independent identity (with a name, an email, and a clear scope of responsibilities).

For example, the product manager only handles requirements breakdown, the designer only produces designs — each does their own thing and then syncs in the channel. It’s not infinite dialogue; it’s division of labor.

Another mechanism that surprised me: every morning the AIs “dream,” reviewing the previous day’s work, figuring out what they did right and wrong, and updating their own behavior rules.

Looking back, the pitfalls I hit with OpenClaw (token waste and agent infinite loops) were essentially because I was treating “multi-agent collaboration” as a prompt chain orchestration problem.

But here, they directly give the AIs identity and responsibility boundaries, making collaboration happen just like between humans. So far, this approach seems spot on.

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