The phrase has two common meanings. In machine learning teams, it means the lifecycle of an AI system: collect data, prepare it, train a model, test it, deploy it and keep monitoring it. Stanford HAI defines AI workflow this way, as the end-to-end process of building, deploying and maintaining an AI system.
In application building, it usually means a fixed chain of AI steps. For example, an inbound email is classified by a model, routed to the right template, drafted by a second model call and checked before sending. Anthropic’s “Building effective agents” draws the line clearly: workflows orchestrate models and tools through predefined code paths, while agents decide their own steps. A workflow that gives the model more control over the path becomes an agentic workflow, and systems that combine several models and tools are often called compound AI systems.
Why it matters: fixed steps can make a workflow easier to test and predict than an open-ended agent, though cost depends on the implementation. Anthropic recommends starting with the simplest approach that works. When someone says “AI workflow,” ask whether they mean the model-development lifecycle or a particular sequence of application steps.
Sources
- Stanford HAI: What is AI workflow? — Academic institute definition; supports the end-to-end building, deploying and maintaining meaning, checked October 24, 2026
- Anthropic: Building effective agents — Engineering guide from a model provider; supports the distinction between workflows with predefined code paths and agents, checked October 24, 2026