Workflow Pipeline

A workflow pipeline is a defined sequence of stages that moves a unit of work from a starting trigger to a finished result. Each stage performs a discrete step and passes its output to the next, so a complex task is broken into ordered, inspectable parts rather than one opaque operation. Pipelines typically include branching, retries on failure, and a recorded run history, which makes them easier to debug, resume, and reason about than ad-hoc scripts.

Pipelines differ from autonomous agents in where the control logic lives. A pipeline encodes the sequence of steps up front — the path is largely known in advance — while an agent decides its next step dynamically as it goes. The two are often combined: an agent may invoke a pipeline as one of its tools, or a pipeline stage may call an agent for a step that needs judgment. Both benefit from idempotency, so re-running a pipeline does not duplicate side effects.

In an AI-workforce setting, pipelines are the repeatable playbooks an industry template ships with — for example, an intake-to-proposal flow or a lead-qualification sequence. Each run is logged as a record so a person can see which stage executed, what it produced, and where a run stalled or was held for approval, giving an operational view of the work the AI agents are doing.