Autonomous AI agents require more than just inference: they need persistent memory. States, histories, embeddings, and past decisions must be reliably available for these systems to operate without human intervention. Today, most of this data resides in centralized databases, creating critical dependencies and single points of failure.
Walrus offers an alternative by enabling AI agents to store and retrieve large volumes of data in a decentralized and verifiable manner. By treating data as programmable blobs, agents can interact with their own memory without relying on centralized infrastructures that may fail, censor, or be altered.
This architecture allows the construction of more resilient AI systems, where the logic can live on-chain while the memory remains distributed and available. For agents that operate continuously and autonomously, storage ceases to be a technical detail and becomes a central component of the design.
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