One under-appreciated property of well-designed onchain reputation is that it compounds over time. A newly created wallet begins with limited history and therefore limited trust. A wallet that has remained active across lending, trading, liquidity provision, staking, and governance for many months generates a denser and harder-to-fake signal. @zerufinance zScore operationalizes this compounding effect at the scale of hundreds of millions of wallets across dozens of chains. Because the score is visible on major explorers and queryable via API, applications can offer differentiated terms immediately, higher loan-to-value ratios for proven wallets, priority access to campaigns, reduced fees, or better incentive multipliers. For users, this creates a clear rational incentive to maintain consistent, positive-sum activity rather than pure short-term extraction. For protocols, it reduces adverse selection and improves the expected quality of participants. The same mechanism extends naturally to AI agents: an agent that repeatedly executes high-quality onchain strategies can accumulate a reputation that subsequent counterparties, protocols, or markets can read and price. In an environment where identity is optional and automated bots are inexpensive to deploy, the ability to measure and price longitudinal behavior becomes a structural advantage for both capital allocators and individual participants.