An agentic workflow is a pipeline of LLM steps whose order you decide. Extract, then classify, then draft, then validate — each step is an LLM call or a tool call, but your code owns the sequence. The model does the thinking inside each step; it does not choose what comes next.
Why it matters in production
This is the reliable middle ground between a single prompt and a fully autonomous agent. Because the path is fixed, a workflow is cheaper, faster, easier to debug, and easier to evaluate step by step. Most tasks that look like they need an agent actually have a knowable shape — and for a known shape, a workflow beats an agent on every axis that matters in production.
The common mistake
Reaching for an agent's autonomy when the task doesn't need it. Every decision you hand the model is a place it can wander, loop, or burn tokens — and most business tasks follow a predictable path you can encode directly. The reverse mistake exists too: forcing a genuinely open-ended task into a rigid workflow it keeps breaking out of. The skill is matching structure to how determined the task really is.
How we actually use it
We default to a workflow and escalate to an agent only when the task is genuinely open-ended, following the narrow-loop playbook of keeping the model on the shortest leash the job allows. Each step gets its own evals, which is far easier when the steps are fixed, and clean context engineering per step keeps the whole pipeline honest.