What Is an Agentic Workflow?

An agentic workflow is a process where AI plans, carries out several sub-steps, reviews the result, and tries again if needed, instead of just answering a question. The model is given tools to fetch data and take actions, and it works in a loop until a goal is achieved. It’s the difference between answering and accomplishing.

The idea becomes clear when you set it against the simple case. A regular AI call is like a single question and a single answer. An agentic flow is more like giving someone a task and trusting them to break it down into steps themselves, check their own work along the way, and come back once it’s done.

A Single Call vs. a Loop with Planning

The fundamental difference lies in the number of steps and in who holds them together.

• Single call. You ask a question, the model answers. One step, no memory of what happened before, no chance to self-correct. Fast and predictable.
• Agentic loop. The model receives a goal, makes a plan, carries out a sub-step, looks at the result, and decides what the next step is. It can use tools and run round after round until the goal is reached or it gets stuck.

It’s the loop and the tools that make the flow “agentic.” The model acts, observes the consequence of its action, and adapts – instead of answering blindly and being done.

A Concrete Example

Picture a case-handling flow in customer service. An agentic workflow might look like this, step by step:

1. Receive the case. A customer gets in touch about a delayed delivery.
2. Fetch data. The agent looks up the order in the system and checks the delivery status via a tool.
3. Propose an action. Based on what it finds, it formulates a proposal: compensate, rebook, or ask for more information.
4. Ask for approval. Before anything actually happens, the agent stops and presents the proposal to a case handler.

The point is that several steps get chained together, with a checkpoint before an action has real effect. That’s precisely what a single call can’t do on its own.

Why More Steps Require More Evaluation

Capability comes at a price. The more steps a workflow has, the more places there are where something can go wrong – and an early error in the loop can carry through and get amplified.

That’s why an agentic setup needs more built around it: logging of what the agent actually did, an evaluation suite that catches when it goes off the rails, and traceability so you can see afterward why it acted the way it did. Without that, an agent becomes a black box that’s sometimes right and sometimes wrong, with no one knowing why.

When an Agentic Setup Pays Off – and When It Doesn’t

It’s easy to get seduced by the idea of an AI that handles the whole process by itself. But more steps aren’t always better, and the choice should be driven by the task.

An agentic flow pays off when the task genuinely requires multiple steps: it needs to gather information from different places, weigh alternatives, and adapt to what it finds along the way. That’s when the loop and the tools add concrete value a single call can’t match.

For simpler tasks, a regular call is often the better choice. If the model just needs to answer a question or rephrase a text, there’s no gain in building a loop around it – just more that can go wrong and a higher cost. A good rule of thumb is to start simple and add steps only once the task demonstrably requires it.

What This Means for You as a Buyer

Agentic workflows are powerful when the task genuinely requires multiple steps and checks – but they aren’t free in terms of complexity. A reasonable approach is to start where the value is clear, keep a human in the loop for anything with consequences, and build in traceability from the start.

Want to think through whether an agentic setup fits a process at your company? Read more about our AI services or get in touch with a description of the flow you want to improve.