Agentic Workflows, Explained
What separates an 'AI assistant' from an autonomous agent — and why product teams need to rethink their UX patterns.
“Agent” is the most overloaded word in software right now. Vendors use it to mean everything from a chatbot to a fully autonomous worker. So before we talk about agentic workflows, let’s anchor on a working definition.
What makes a workflow “agentic”
An agentic workflow has three properties that an assistant flow doesn’t:
- A goal, not a turn. The system is given an outcome and figures out the steps. It isn’t waiting on the next user message.
- Tools and side effects. It calls APIs, writes to systems, makes decisions that change state in the real world.
- A loop with feedback. It plans, acts, observes, and re-plans — possibly many times — until the goal is satisfied or it hits a guardrail.
The new UX problems
When you let a system act on its own, you inherit a new set of design problems:
- Visibility into intent. Users need to see what the agent is trying to do before it does it.
- Steerable autonomy. Some tasks should be fully automated; others need a human in the loop. The product has to expose that dial.
- Reversibility and audit. Every side effect needs to be observable, attributable, and — when possible — undoable.
A pattern that works
Treat the agent as a colleague who emails you their plan. Show the plan. Let the user approve, edit, or take over. Run it. Show the result. That single pattern — propose, confirm, execute, report — solves 80% of the trust problem in enterprise contexts.