Prompt Chaining vs Agentic Workflows

As teams build with large language models, two patterns come up constantly: prompt chaining and agentic workflows. They sound similar and sometimes overlap, but they represent different philosophies about how much control to hand the model. This article clarifies what each one is, how they differ, and how to decide which fits a given problem.

What Prompt Chaining Is

Prompt chaining is the practice of breaking a task into a fixed sequence of steps, where the output of one prompt becomes the input to the next. A developer designs the chain in advance: first summarize the document, then extract the key points, then draft a response based on those points. Each step is a discrete call to the model with a specific purpose, and the order is determined by the developer, not the model. The model fills in each step, but it does not decide what the steps are or whether to deviate from them.

This approach is predictable and easy to reason about. Because the control flow is hard-coded, you know exactly what will happen and in what order, which makes prompt chains straightforward to test, debug, and trust. The trade-off is rigidity: a prompt chain handles the path it was designed for, and only that path. If a task requires a different sequence depending on what the model finds along the way, a simple chain cannot adapt.

What an Agentic Workflow Is

An agentic workflow hands meaningful control to the model itself. Rather than following a predetermined sequence, the agent is given a goal, a set of tools, and the autonomy to decide what to do next. It operates in a loop: it reasons about the situation, chooses an action, observes the result, and decides on the next action based on what it learned. The sequence of steps is not fixed in advance; it emerges from the agent's decisions as it works toward the goal.

This flexibility is the defining advantage. An agentic workflow can handle tasks where the right steps depend on circumstances that cannot be known ahead of time, can recover from unexpected results by trying a different approach, and can tackle open-ended problems that resist a single fixed recipe. The cost of this autonomy is reduced predictability. Because the agent decides its own path, its behavior is harder to anticipate, test exhaustively, and constrain, and it can take wrong turns that a fixed chain would never take.

The Core Difference: Who Decides the Path

The cleanest way to separate the two is to ask who controls the flow of execution. In prompt chaining, the developer controls the path and the model executes individual steps. In an agentic workflow, the model controls the path and the developer provides the goal, the tools, and the guardrails. Everything else follows from this distinction. Prompt chains trade flexibility for reliability and transparency; agentic workflows trade reliability and transparency for flexibility and adaptiveness.

It is worth noting that these are ends of a spectrum rather than a strict binary. Many real systems blend them, using a mostly fixed workflow with one or two points where the model is allowed to make a decision or loop until a condition is met. Recognizing where on this spectrum a system sits helps clarify how much control has actually been delegated.

Choosing Between Them

The right choice depends on the task and the tolerance for unpredictability. When the steps are known, stable, and repeatable, prompt chaining is usually the better fit, because it delivers the result with far less risk and is easier to maintain. When the task is open-ended, varies case by case, or requires the system to adapt based on intermediate findings, an agentic workflow earns its added complexity. A useful default is to start with the simplest approach that could work, often a chain, and add agentic autonomy only where the problem genuinely demands it, since the extra power comes with extra cost in oversight and testing.