Which of the following best describes an agentic workflow?

An agentic workflow is characterized by its dynamic multistep nature, allowing for the breakdown of complex tasks into manageable components. This approach emphasizes adaptability and responsiveness, as agents within the workflow can make decisions, adjust their actions based on changing circumstances, and navigate through various processes in a manner that mirrors human problem-solving capabilities. By segmenting complex tasks into simpler steps, an agentic workflow facilitates greater efficiency and effectiveness in achieving goals, making it particularly useful in AI applications where flexibility and learning from interactions are essential.

In contrast, the other descriptions do not capture the essence of an agentic workflow. Static processes imply a lack of flexibility and decision-making capabilities, whereas a process operating solely on rules lacks the adaptive learning that characterizes an agentic approach. A linear sequence of predefined steps also suggests rigidity, failing to allow for the necessary adjustments that can emerge in a dynamic environment. Thus, the most accurate portrayal of an agentic workflow is one that embodies flexibility and the ability to manage complexity through a multistep process.