An agentic workflow is a business process where AI agents plan the steps, use tools, and check their own work to reach a goal — instead of following a fixed script. Unlike linear automation, which runs the same steps every time, an agentic workflow adapts to context, handles exceptions, and coordinates hand-offs between agents, systems, and people.

Agentic context engineering — building the infrastructure that captures, distills, and routes your business knowledge to the right agent at the right moment — is the real moat in agentic operations. The model is a commodity. Every company will have access to the same frontier models, the same orchestration frameworks, the same agent toolkits. What they will not have is your organization's accumulated context: the decisions made in Tuesday's client call, the constraint surfaced in an internal chat, the process refinement your team discovered two quarters ago.

An agentic operating system (agentic OS, or AOS) is the operating model a business runs on when AI agents—not just people or scripts—do the work: a coordinated layer of agents, shared context, guardrails, evaluations, and orchestration that turns one-off AI experiments into a system that ships real business outcomes on repeat.

Agentic AI is software given a goal that it pursues across many steps on its own — planning the work, using tools, checking its results, and correcting course — without a person driving each move. Where generative AI produces an output when prompted, agentic AI runs an entire process to completion, deciding each step itself within boundaries you set.