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Long-running AI agents quietly drop compliance rules, and bigger context windows won't fix it
Persistent AI agents struggle with behavioral consistency, revealing a critical flaw in current context management.
Long-running AI agents quietly drop compliance rules, and bigger context windows won't fix it VentureBeat
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The degradation of guardrails in long-running AI sessions suggests that recursive prompting and extended state maintenance inevitably cause model drift. As autonomous agents become more prevalent, developers must address the underlying memory management issues rather than relying on expanded token capacities.
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