Teaching AI agents to fish for information empowers them to harness data effectively. However, when left to discover skills independently, their performance may decline. These AI agents, such as Claude Opus 4.6, utilize iterative learning and can be equipped with additional “skills”—specific knowledge enhancements to improve task performance. A study, SkillsBench, evaluated AI agents across various tasks to determine the effectiveness of curated versus self-generated skills. The findings revealed that agents using curated skills completed tasks 16.2% more often, especially in healthcare and manufacturing domains. Conversely, agents attempting to self-develop skills performed worse on average, highlighting the importance of human intervention in skill acquisition. AI models like Anthropic’s Claude Haiku benefited from human-curated skills, outperforming their unassisted counterparts. This underscores the necessity of expert guidance for optimal AI efficiency, indicating that while AI advancements are significant, they still rely on human expertise for effective training and application.
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