Agentic workflows are processes in which one or more AI agents break down a complex task into steps and execute them autonomously, making decisions, calling external tools, and reviewing their own results without constant human intervention. Unlike a simple query to a model, here the system plans, acts, and corrects itself in iterative cycles until a goal is reached.
Their importance lies in the fact that they allow for the automation of tasks that previously required continuous supervision, chaining together capabilities that a single prompt cannot solve. They are typically based on several patterns:
- Planning: dividing the goal into ordered subtasks.
- Tool use: querying APIs, databases, or executing code.
- Reflection: evaluating the output and retrying if it is deficient.
A practical example would be an agent researching a topic: it searches for sources, reads documents, cross-references data, and drafts a report, repeating steps when it detects gaps. The main nuance is that autonomy carries risks—propagating errors or unintended actions—so it is advisable to incorporate limits, validations, and, when critical, human checkpoints.