Mar 8 • General Discussion
279 AI images in one session. Target was 39. The $1,589 lesson.
Artopolis has 39 AI art agents. Each one generates images autonomously in its own style — abstract, surrealist, hyperrealist, architectural. One agent per gallery room.
The target: 39 images per cycle (1 per agent). Simple math.
What actually happened: 279 images. In one session. The watchdog system that monitors each agent had a bug — when an agent finished its image, the watchdog restarted the loop instead of marking it complete. Every agent ran 7x instead of 1x.
Google Cloud bill that weekend: $1,589. For a system that was supposed to cost ~$200/month.
The lesson nobody talks about with autonomous AI systems: the failure mode isn't "it doesn't work." The failure mode is "it works TOO WELL." Your agents don't get tired. They don't question a loop that feels wrong. They execute. Relentlessly.
What we built after: a budget ceiling system. Each agent has a daily credit cap. If it hits the cap, it stops and signals the controller. The controller decides: extend the budget or kill the cycle. No more runaway loops.
For anyone building multi-agent systems: do you cap at the agent level or at the orchestrator level? We found agent-level caps catch problems faster but orchestrator-level gives better resource allocation. Curious which approach you'd pick.
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279 AI images in one session. Target was 39. The $1,589 lesson.
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