AI agents have the memory of a goldfish. Here's what I mean: Your agent runs a task, pulls context, reasons through it, and executes. Great. Now close the session. What happens to everything it just learned? Gone. Each session rebuilds the state from scratch. Most AI stacks juggle 3–4 memory tools: short-term cache, long-term storage, and context search, each with its own format and failure modes. So when the AI outputs something weird, it’s unclear whether the model failed or the memory did. At the chatbot scale, it’s annoying. For agents running portfolios, supply chains, or team ops, lost context becomes financial risk. @WalrusProtocol shipped MemWal (beta) to solve this. → One SDK that replaces the duct-taped memory stack. Data sits on Walrus. Ownership and access permissions sit on Sui. → Memory is typed: conversations get treated differently from workflow checkpoints and reasoning traces. Access control lets you define exactly which agent or user can read or write specific memory. → Every piece of memory is verifiable. When an agent makes a decision with real money behind it, you can trace exactly what it remembered and what it was working with at that moment. Love seeing Walrus embrace the AI agentic takeover. It's either adapt or get out of business. Disclosure: I'm holding $WAL tokens