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Agentic Workflows vs. Agents — when to fix, when to free

Redaktion ·

“Agentic workflow” and “agent” get used interchangeably all the time — but they’re two different things. The difference decides how reliably your system runs and how much control you keep.

Anthropic, the company behind the Claude model, drew a clean line between them. That’s the line we’ll walk through here.

The two terms — short and clean

Picture a system that runs several AI steps one after another. There are two fundamentally different ways to build that.

Workflows are systems where language models and tools are steered through hard-wired code paths. You, the developer, decide up front: first step A, then B, then C. The model fills each step with content, but the order is fixed. Anthropic calls this “orchestrated through predefined code paths.”

Agents are systems where the model itself decides what happens next. It chooses its own steps, calls tools when it needs them, and stays in control of how it solves a task. Anthropic: “LLMs dynamically direct their own processes and tool usage.”

So the difference isn’t complexity — it’s who decides. In a workflow, you decide. In an agent, the model decides.

Why this matters at all

It sounds like a fine point, but it sits at the heart of every serious decision about AI systems. Because two properties hang off this line, and they pull against each other.

A workflow is predictable. You know in advance which steps run, in what order, with which tools. That can be tested, debugged, and monitored. When something goes wrong, you know where.

An agent is flexible. It can handle tasks whose exact number of steps you can’t even know in advance. But you give up control. The agent can take paths you didn’t foresee — good ones and bad ones.

The fixed workflow patterns

If you go the workflow route, there are a few proven shapes. Anthropic describes five of them:

  • Prompt chaining — A task is split into fixed sub-steps. Each model call processes the output of the previous one. The classic case: draft a text, then translate it, then shorten it.
  • Routing — The input is classified first, then sent to the matching specialist path. A support request goes to a different branch depending on its topic.
  • Parallelization — Independent sub-tasks run at the same time instead of one after another. Or the same task runs several times and the results are voted on.
  • Orchestrator-workers — A central model breaks the task down and hands sub-pieces to several worker models. More on this in the entry on multi-agent patterns.
  • Evaluator-optimizer — One model produces an answer, a second one grades it and gives feedback. This loops until the result is good enough.

Here’s the thing: these patterns are all predefined. The code decides how the model calls are chained. The model reasons within each step, but it doesn’t jump out of the path.

When do you use which?

Here’s the order that works in practice — and the one Anthropic recommends too: start as simple as you possibly can.

First, a single model call. For many applications, one well-written prompt is enough — maybe with a couple of examples and a tool. Anthropic puts it plainly: “optimizing single LLM calls with retrieval and in-context examples is usually enough.” No workflow, no agent — just a good call.

Then a workflow, when the task is well-defined and you need reliability. Always the same steps, always the same output format. Checking invoices, rewriting text to a fixed template, extracting data. Workflows “offer predictability and consistency for well-defined tasks.”

Only then an agent, when you can’t predict the number of steps and the model has to decide for itself. Open-ended research, complex coding tasks, situations with many possible paths. Agents are the choice when “flexibility and model-driven decision-making are needed at scale.”

The most common mistake

A lot of people build an agent straight away, because it sounds like the more powerful tool. And that’s exactly where it clicks once you’ve understood the trade-off: the agent isn’t “better,” it’s just freer. For a task with clear steps, that freedom is a downside — it brings unpredictability where you needed reliability.

If you know exactly which steps are required, wire them up. That’s a workflow, and it will run more reliably than any agent you hand the same task to.

If you want to go deeper into building your own agents, the details live in the entry on building AI agents. For the tooling side, see AI agent frameworks at a glance.

FAQ

Is an agentic workflow the same as an agent?
No. A workflow follows hard-wired code paths that you define in advance. An agent decides its own steps and tool usage. The difference is who holds control — you or the model.
Which one is more reliable?
The workflow. Because the steps are predefined, it's predictable, testable, and easier to debug. An agent is more flexible but less predictable in return.
When should I use an agent instead of a workflow?
When you don't know the required steps up front. Open-ended tasks, where the number of steps only emerges during the work, are the typical case for an agent. For clearly defined tasks, a workflow is the better choice.
Do I even need a workflow or an agent at all?
Often not. For many applications, a single well-built model call with the right context and one tool is enough. Start there, and only level up when a single call no longer suffices.
Where does this definition come from?
From Anthropic, the company behind the Claude language model. They drew a clean line between workflows and agents in their article "Building Effective Agents" — and that definition has become the industry reference.

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