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One of the well defined failure modes for AI agents/models is "laziness." Yes, models can be "lazy" and that is an actual term used when reviewing them.

I am not sure if we know why really, but they are that way and you need to explicitly prompt around it.

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I've encountered this failure mode, and the opposite of it: thinking too much. A behaviour I've come to see as some sort of pseudo-neuroticism.

Lazy thinking makes LLMs do surface analysis and then produce things that are wrong. Neurotic thinking will see them over-analyze, and then repeatedly second-guess themselves, repeatedly re-derive conclusions.

Something very similar to an anxiety loop in humans, where problems without solutions are obsessed about in circles.

yeah i experienced this the other day when asking claude code to build an http proxy using an afsk modem software to communicate over the computers sound card. it had an absolute fit tuning the system and would loop for hours trying and doubling back. eventually after some change in prompt direction to think more deeply and test more comprehensively it figured it out. i certainly had no idea how to build a afsk modem.

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