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For a long time, building AI meant building bigger and better datasets. The logic was simple: if a model performed poorly, give it more examples, more annotations, more carefully labeled training data. These systems could be impressive in a narrow scope, such as classifying images, tagging text, and predicting outcomes, but they were only as good as the examples they see. Step even slightly outside the distribution of training data, and performance collapsed.

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Real-World Domain-Specific Use Cases of Agentic AI

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  • 发表日期：

    2026年

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  • Dhivya Nagasubramanian

摘要

For a long time, building AI meant building bigger and better datasets. The logic was simple: if a model performed poorly, give it more examples, more annotations, more carefully labeled training data. These systems could be impressive in a narrow scope, such as classifying images, tagging text, and predicting outcomes, but they were only as good as the examples they see. Step even slightly outside the distribution of training data, and performance collapsed.