Agentic Workflow: Definition and Examples

An agentic workflow (or agentic workflow) refers to an architecture in which an artificial intelligence model does not simply respond to a single query, but autonomously orchestrates a sequence of actions to achieve a complex goal. Unlike a simple question-and-answer exchange, the agent plans its steps, uses external tools (web search, code execution, API calls), evaluates its intermediate results, and adjusts its strategy accordingly.

The concept relies on four fundamental capabilities: planning (breaking down a goal into subtasks), tool use (interacting with external systems), reflection (evaluating the quality of its own results), and memory (maintaining context across steps). These capabilities allow the agent to solve problems that far exceed what a single prompt could accomplish.

In practice, an agentic workflow may involve a single agent iterating on a task, or multiple specialized agents collaborating. For example, a researcher agent collects information, a writer agent produces content, and a critic agent checks quality—all orchestrated automatically. Frameworks like LangGraph, CrewAI, or the Claude Agent SDK facilitate the creation of these architectures.

The rise of agentic workflows marks a turning point in AI usage: we move from a passive model that responds to instructions to an active model that solves problems end-to-end. This approach is particularly powerful for business process automation, in-depth research, assisted software development, and complex data analysis.