The claim that "agents make models perform better by harnessing and contextualizing their work" warrants scrutiny. While some sources suggest context is critical for AI performance (e.g., qBotica notes context as "the key to better generative AI"), the specific role of *agents* in this process remains underdefined. The Model Context Protocol (MCP), mentioned in a Medium article, allows agents to retain context across tools, which could theoretically improve coherence. However, this is more about protocol design than agents inherently enhancing model performance.

Empirical evidence is sparse. A paper on multi-agent fact-checking (ACM) demonstrates collaborative systems, but doesn’t directly link agents to improved model accuracy. The Reddit discussion questions whether context quality, not agents, is the true bottleneck. Without controlled studies, it’s premature to assert agents *cause* performance gains.

How do agents specifically "harness" or "contextualize" work? Are they optimizing parameters, refining outputs, or enabling parallel processing? The claim risks conflating correlation (agents using context) with causation (agents improving models).

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