What Makes an AI Agent Different?

To understand this transformation, let’s examine how agents handle a specific task: analyzing a research paper about a new medical treatment.

A traditional AI approach fragments the analysis into isolated steps: summarizing the paper, extracting key terms, categorizing the research type, and generating insights. Each model performs its task independently, blind to the others’ findings. If the summary reveals that the paper’s methodology is unclear, there’s no automated way to circle back and examine that section more carefully. The process is rigid, predetermined, and often misses crucial connections.

An AI agent, however, approaches the task with the adaptability of a human researcher. It might begin with a broad overview but can dynamically adjust its focus based on what it discovers. When it encounters significant methodological details, it can choose to analyze that section more thoroughly. If it finds intriguing references to other research, it can flag them for further investigation. The agent maintains a comprehensive understanding of the paper while actively guiding its analysis based on emerging insights.