An AI workflow is a multi-step process that uses AI models for one or more of its steps. The workflow defines inputs, outputs, transitions between steps, and human checkpoints. It is the operational unit that turns a model call into a business process.
An AI workflow is distinct from an AI agent. A workflow is a structured pipeline with defined steps. An agent can dynamically plan its own steps. Most production AI deployments are workflows, not agents — explicit step-by-step pipelines are easier to test, monitor, and govern.
For engineering teams, AI workflows should be treated like any production system: version-controlled, observable, testable, and owned. The fact that one step calls a model does not change the engineering discipline required.
Why AI workflow Matters for Distributed Teams
AI workflows are where AI moves from demo to production. The teams that build durable AI value treat workflows as software systems, not as one-off prompts.
Frequently Asked Questions
What is an AI workflow?
An AI workflow is a multi-step process that uses AI models for one or more steps. It defines inputs, outputs, transitions between steps, and human checkpoints. It is distinct from an AI agent, which can dynamically plan its own steps.
Related Terms
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AI orchestration is the coordination of multiple AI models, tools, and steps into a coherent workflow. It includes routi...
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