Agent Amnesia Crisis: Persistence and caching technologies become key for data foundation

As the context capacity of the Agent model increases, the problem of memory stability becomes more prominent. Although some reports suggest that large context capacity can solve the memory issue, actual tests show that the effective context utilization is far lower than the nominal value. NVIDIA RULER benchmarks indicate that the effective context utilization is only 50%–65% of the nominal value, and there is a phenomenon of ‘context decay’, causing the Agent to gradually lose its memory over long running times. To address this challenge, persistence and caching technologies are considered core solutions for building a reliable data foundation for the Agent, aiming to compensate for the internal memory degradation of the model through external storage mechanisms.