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This article discusses the concept of 'prompt debt' in AI applications, where over-reliance on natural language prompts leads to brittle, hard-to-maintain systems that become locked to specific models.
The author reflects on the limitations of using flat markdown files for long-term agent memory, which leads to prompt debt as the memory grows, and advocates for graph-based memory representations that retrieve relevant context dynamically.