Where Experience Should Live: Context, Cartridge, or Weights

Ninety Days of Agent Work explores the intricacies of using different storage mechanisms for improving AI model performance, focusing on contexts, cartridges, and weights. The text highlights the transient nature of context as a storage space for instructions and the challenges in fine-tuning models, which affects all users. Cartridges, described as small add-ons, excel in encoding new capabilities without altering the base model, though they struggle with open-ended tasks. Weights, on the other hand, are better suited for complex and open-ended conversations due to their adaptability across requests. Experiments revealed that cartridges could achieve high success in novel tasks but face limitations in capacity and interference when combining multiple skills. The article also discusses the importance of maintaining task isolation and the potential for cartridges to degrade performance on unrelated tasks. It emphasizes the need for careful engineering to ensure optimal usage of these storage mechanisms, highlighting the trade-offs between cost, persistence, and cross-domain risks.

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