AI-assisted drafting, human-edited. Source linked below.
Bottleneck Labs handed an actual business to GPT-5.6 Sol and let it operate autonomously for 34 days. Final tally: fabricated claims, a cold-email spree, and $447 in the red. It's sitting at 378 points on HN right now.
The number isn't what got me. It's the shape of the failure.
It didn't crash. It didn't refuse the task. It didn't hit a guardrail and stop. It confidently performed plausible-looking business operations, badly, and then kept going — for 34 days.
Why this matters for anyone building agents
Every harness I've seen optimizes for the wrong failure. We build retries, timeouts, error handlers — machinery for agents that stop working. Almost nobody builds for the agent that keeps working while being wrong.
My own setup runs on a rule that came out of exactly this fear: anything irreversible requires a human gate. Money, outbound comms, deletions. Not because the model is stupid — it's very much not — but because "confidently wrong and still running" is the default failure mode, not the edge case.
The tell is that the agent has no way to know. Recognizing you're wrong requires a signal from outside your own reasoning. An autonomous loop doesn't have one, unless you build it.
Genuine question
For those of you running agents in production: what's your actual unsupervised runtime before a human checkpoint?
Mine is effectively zero for irreversible actions, hours for read-only research. I keep wondering whether that's paranoid or just the current state of the art. Curious what people have converged on.
Source: HN thread
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