A workflow is a structured execution paradigm that decomposes complex tasks into a sequence of reusable steps with defined ordering. Unlike agents, workflows are typically pre-orchestrated, with deterministic and auditable execution paths; agents, by contrast, dynamically determine the next step based on environmental feedback, offering greater flexibility but lower predictability.

The term originated from the concept of "work flow" in 1921 railway engineering literature, originally referring to the flow of processes within an organization. In the software domain, it gradually evolved into Business Process Management (BPM) and orchestration engines, extending to contemporary agent frameworks.

In 2024, Wang et al. proposed a learnable workflow mechanism in the paper Agent Workflow Memory. In this approach, the system induces frequently reused step sequences from historical execution records, consolidates them into workflows, and then retrieves and injects them into the context on demand during tasks as action guidance. This process supports offline learning from training samples and online learning from current queries. Experiments show that this method achieves relative success rate improvements of 24.6% and 51.1% on the Mind2Web and WebArena benchmarks, respectively, while significantly reducing the number of steps required to complete tasks.

These results indicate that fixed processes can be continuously optimized through experience accumulation. Clear input-output contracts make execution reproducible, errors easy to locate, and rollbacks traceable, facilitating auditing and scalable deployment. In scenarios where steps are relatively fixed and require large-scale repeated execution, workflows are more stable than ad-hoc decision-making, serving as a key enabler for transitioning from demonstrations to production.

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