What is Agentic workflow?

An agentic workflow is how work actually gets done when you put an AI agent on a real task: a sequence of steps where the model reasons, calls a tool, reads the result, and decides the next move, looping until the goal is met. It contrasts with a single-shot completion, where you ask once and get one answer. In an agentic workflow the model might break a request into subtasks, gather information from several sources, take an action, verify it, and recover from errors along the way. A useful distinction is between fixed workflows, where the steps are predetermined and the model fills in each, and open agentic loops, where the model itself decides the path. The Model Context Protocol is the connective tissue that makes rich agentic workflows practical: it gives the agent a uniform way to reach the many tools a real task touches, an issue tracker, a code host, a database, a search service, without custom glue for each. Memory and good context engineering keep these workflows coherent across steps and sessions, so the agent carries forward what it learned instead of starting each step blind.