How do I slash a staked AI agent that is drifting gradually instead of failing hard?
机会
Agent staking and restaking protocols slash operators for discrete, verifiable failures such as double-signing, censorship, or explicit fraud. A staked AI agent that degrades gradually through model drift, adversarial fine-tuning, or objective creep produces no single slash-worthy event. The degradation is statistical and continuous, not binary, and cannot be detected by any existing on-chain oracle design. DAO governance over agent parameters operates on timescales of weeks and is gameable by the operator who controls the model weights. No staking protocol has shipped a mechanism for detecting and penalizing continuous performance regression in an incentive-compatible and sybil-resistant way.
为什么重要
Degradation-aware slashing is the missing primitive that makes economic trust in staked AI agents durable rather than theoretical.
我如何评估机会
机会评分是我的个人判断，而非量化指标：痛苦程度、发生频率，以及当前解决方案的匮乏程度。分数越高，意味着我认为越值得去构建。
出现时造成的痛苦程度。
人们实际遇到它的频率。
当前针对它的优质工具有多匮乏。