Trustless AI agents are quickly moving from concept to reality, powered by the rise of privacy-preserving, secure-compute infrastructure. Instead of relying on traditional systems where user data is exposed to servers, databases, or infrastructure operators, next-generation AI agents can now operate inside hardware-protected Trusted Execution Environments (TEEs). These enclaves allow computation to happen in a sealed environment where neither node operators nor external systems can view or extract the data being processed. By combining AI systems with secure compute layers, agents gain the ability to manage highly sensitive operations such as private key handling, user memory, and contextual decision-making without ever exposing raw information outside the enclave. This fundamentally shifts the trust model: users no longer need to trust infrastructure providers with their data, because the data is cryptographically and hardware-protected by design. Technologies developed within the @OasisProtocol ecosystem are helping push this model forward by enabling confidential AI memory systems. In this setup, retrieval, storage, and inference all occur within secure enclaves, ensuring that sensitive user context remains fully isolated from external visibility at every stage of processing. The implications are significant: ✔️AI memory becomes private by default ✔️Private keys can be managed securely within execution environments ✔️Autonomous agents can operate without relying on trusted intermediaries This creates a new class of AI systems, trustless, autonomous, and privacy-preserving, where users maintain full ownership and control over their data while still benefiting from intelligent, always-on agents. In Web3 and AI convergence, privacy is no longer an optional feature, it becomes the core foundation of trust, security, and adoption. @OasisProtocol $ROSE 🚀