I Ran Hermes Agent on the Same Task for 7 Days. The Skill File on Day 7 Looked Nothing Like Day 1.

Editor's take

Hermes, a novel AI agent framework, demonstrated significant skill drift over a week of continuous operation on a single task, with its internal "skill file" undergoing substantial modification. This observation underscores the challenge of maintaining stable, predictable AI behavior in long-running, autonomous systems, particularly for agents that learn and adapt in real-time.

The implications are far-reaching for applications requiring reliability, such as automated customer service bots or industrial process control. The rapid evolution of Hermes's skill file, without explicit retraining or explicit human intervention, raises questions about interpretability and control, especially when compared to the more static skill sets of models like GPT-4, which are typically updated through discrete training cycles.

Future developments will focus on understanding the mechanisms driving this skill evolution and whether it can be steered towards beneficial outcomes. Key questions include whether this drift is a sign of robust adaptation or emergent instability, and if techniques can be developed to anchor or guide this learning process to prevent undesirable performance degradation or the acquisition of unintended behaviors.