Agent Smith Commerce — Private Intelligence, Public Action

Problem Statement

AI agents handling commerce need access to sensitive data — pricing strategies, financial positions, personal preferences — but current agent architectures expose all context to inference providers. There is no privacy-preserving way for agents to reason over confidential data and then act publicly on-chain. This creates a fundamental tension between agent capability and data privacy. By combining Venice private inference with Self ZK identity and Locus controlled payments, the agent keeps secrets while maintaining trust.