ended4월 2일· 1 sources
LLM Agents Fail Silently—A Case for Real-Time Behavioral Monitoring
LLM 에이전트는 조용히 변질한다, 행동 감시가 답이다
Why it matters
LLM agents don't crash—they degrade. While traditional frameworks check output correctness, agents can silently deviate from intended behavior through scope creep, semantic drift, and confidence collapse. Continuous behavioral monitoring is essential for AI safety as autonomous agents increasingly make unsupervised, high-stakes decisions.
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LLM agentsbehavioral driftagent safetyanomaly detectionentropy signals