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AI governance needs fences not sandboxes, says operator running 50-agent cluster
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An operator running approximately 50-60 Claude AI agents at scale argues that AI systems should be governed by laws ("fences") rather than containment programs ("sandboxes"), based on daily experience that even top-tier models make poor autonomous decisions.
Even the most capable publicly available models make judgment errors comparable to a sixth-grader when operating unsupervised, which challenges the industry's focus on technical containment as the primary safety mechanism. The author's position is that restrictive sandboxing fights against how these models work best, and that legal or rule-based governance may prove more effective and scalable.
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The author runs 50-60 AI agents using Claude Fable 5, spending the equivalent of roughly $122k/month in API tokens (about $5k/month out of pocket with individual discounts) on a 512GB M3 Ultra Mac Studio.
Even Fable, described as the best model most can access, makes at least one terrible decision daily when left unsupervised, with judgment comparable to a sixth-grader despite exceptional coding and analysis abilities.
The prevailing industry approach of sandboxes, guardrails, and context rationing fights against the grain of how capable models work; governance by laws ("fences") may be more effective than technical containment.
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