The Knowledge Chipper: An Agentic Coding Story

AI coding agents accumulate vast amounts of contextual knowledge while working on problems, but this information is almost entirely lost after each session ends—a significant waste of institutional knowledge and efficiency, especially when different team members or LLMs need to restart work on the same code. The author argues that LLM portability (the ability to seamlessly switch between different AI models while preserving context) is not merely a theoretical concern but a practical necessity, highlighted by real-world scenarios like regional AI service disruptions and teams using different AI tools that must collaborate effectively.