Shared Selective Persistent Memory for Agentic LLM Systems

Introduced shared selective persistent memory for agentic LLM systems, categorizing reusable context (task specs, data schemas, tool configs, output constraints) and discarding session-specific reasoning traces. Implemented this in a collaborative workspace platform with git-versioned artifacts. Developed a zero-token data refresh mechanism to decouple generated programs from runtime data, enabling artifact reuse without re-invocation. Demonstrated significant improvements in task completion (96% vs. 79% without memory, 71% with full history), reduced token costs (97x reduction), and task time reduction (14x) for recurring data updates.