An agentic workflow is a sequence of steps an AI agent carries out toward a goal, choosing actions along the way instead of returning one response. Where a chatbot answers a question, an agentic workflow might watch for an event, read the relevant context, draft a response, and execute it across tools — looping until the goal is met.
The defining traits are autonomy and tool use. The agent decides the next step, calls APIs or applications to do real work, and can pause for human approval before anything irreversible. That confirm-before-execute checkpoint is what separates a useful agentic workflow from an unpredictable one.
In an operator context, agentic workflows replace the manual hand-offs between tools — a lead comes in, the agent enriches it, drafts the outreach, schedules the follow-up, and updates the CRM, surfacing each consequential action for sign-off rather than forcing a human to stitch five apps together.
Read next
Frequently asked
How is an agentic workflow different from automation?
Traditional automation (like a Zapier zap) follows fixed if-this-then-that rules you define. An agentic workflow lets the agent decide the steps and adapt to context, using tools as needed — better for fuzzy, multi-step tasks that rigid rules handle poorly.
Are agentic workflows safe to run unsupervised?
The safe pattern is confirm-before-execute: the agent drafts and plans autonomously but pauses for human approval before consequential or irreversible actions. Full unsupervised autonomy is reserved for low-risk, reversible steps.
More from Agentic AI
All termsAI Agent
Software that decomposes a goal into multi-step actions, executes those actions across applications, and reports back, with optional human approval at each step.
ReadHuman-in-the-Loop
A design where an AI agent drafts and proposes, but a person approves before consequential actions execute.
ReadMCP (Model Context Protocol)
An open standard that lets AI models connect to tools and data sources through a common interface.
Read