An agentic workflow combines fixed, engineer-defined execution paths with dynamic, model-driven decision making. It uses code-mandated steps, branches, and loops for predictable outcomes, while allowing the model to determine the sequence or content of tasks where flexibility is beneficial. This blend ensures reliability for core processes and adaptability for novel situations. Systems that must stay predictable tend to keep most of the path fixed and give the model only the steps where flexibility pays.
The practical reality is that most systems require predictable outcomes for critical functions; a fully agentic path means constant, costly runtime verification. An agentic workflow applies agents only to the parts of a process that truly benefit from dynamic decision-making, like interpreting nuanced user requests or resolving ambiguous data. This allows engineers to guarantee predictable behavior for the bulk of the work, while still leveraging the model's ability to navigate unexpected inputs or generate creative solutions in specific stages.
The key nuance is distinguishing where the execution logic resides. Unlike simple task routing or discrete automation, which primarily connect predefined steps, an agentic workflow deliberately assigns decision-making to the model only when flexibility adds sufficient value to outweigh the cost of runtime verification. This means the model's choices are always constrained by the encompassing harness, which provides the necessary guardrails and validation. The more of the path the model chooses, the more comprehensive those runtime checks must be to maintain predictability and trust.