How to Maximize Agentic Memory for Continual Learning

LLMs often require users to repeat instructions in new sessions, which is inefficient. A proposed solution for continual learning is to use a file, such as `agents.md`, to store persistent information like user preferences, coding standards, and environmental details. This file is then fed to the agent at the start of new tasks, preventing the 'cold start' problem and making interactions more effective. The process involves regularly updating this 'agentic memory' file with generalized knowledge from each session, which ultimately saves time and reduces costs by minimizing redundant queries.