An agentic workflow is an advanced AI-driven execution model in which an intelligent agent—typically powered by a large language model—actively reasons about a goal, breaks it into sub-tasks, selects appropriate tools or actions, executes them, and continuously adapts based on results.

Unlike traditional workflows (like ETL pipelines or CI/CD), which are static and predefined, agentic workflows are:

• Dynamic → Steps are not fixed; they evolve during execution
• Goal-driven → The system focuses on outcomes, not predefined paths
• Iterative → The agent observes results and refines its actions
• Tool-augmented → It can call APIs, query databases, run scripts, or interact with systems (e.g., Kubernetes, OpenShift, AWS)
• Context-aware → Uses memory (short-term + long-term) to improve decisions