Giving AI Agents Permanent Memory with MCP and CRDTs
Introduction: The Missing Layer in AI Systems
In the last two years, AI agents have crossed the line from research demo to production infrastructure. Coding assistants like Claude Code and Cursor ship to millions of developers. Customer-support agents resolve tickets end-to-end. Autonomous research agents browse, summarize, and act. The capability curve is steep and still climbing.
And yet, underneath the sophistication, a structural limitation persists: most agents are stateless. Each conversation, each task, each invocation begins from zero. The model is powerful; the system wrapping the model is amnesiac.
This is not a limitation of the model weights. A modern LLM can reason about hundreds of thousands of tokens in a single context. The limitation lives one layer up — in the architecture that decides what the model sees, what it persists, and how that state survives across sessions, users, and agents. In short: AI has become competent in the...
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