Agentic Workflows vs AI Agents: Which One Does Your Business Actually Need?

An agentic workflow runs a job through steps a person designed, with AI doing the thinking inside some of them. An AI agent decides its own steps towards a goal. Most businesses should start with workflows, because they are predictable, cheaper and easier to check. Agents earn their cost only where the steps genuinely cannot be planned.

What is an agentic workflow?

An agentic workflow is a defined sequence of steps where AI handles one or more of them: reading information, drafting, checking or deciding between a small number of options. The overall path is set in advance by a person. The AI brings judgement inside each step, but it does not decide what happens next on its own.

A weekly client report is a typical example. First, the figures are pulled. Next, AI compares them with targets and drafts commentary in the house style. AI then checks the draft against a checklist. Finally, a person approves it. Each step is clear, so the output is predictable and easy to review.

Anthropic, the company behind Claude, draws the line precisely. In its December 2024 guidance, it defines workflows as "systems where LLMs and tools are orchestrated through predefined code paths". Agents, by contrast, are systems where the model directs its own process and tool use.

What is the difference between a workflow and an agent?

The difference is control over the path. A workflow follows a route someone designed, so it behaves the same way each time. An agent plans and adapts its own route, so it can handle surprises but is less predictable, costs more to run and is harder to check.

Neither is "smarter". A workflow can include sophisticated AI steps. The question is simply whether the job's steps are known.

Which do most businesses need?

Most businesses need workflows first. The jobs that waste the most time, such as reports, proposals, follow-ups, content drafts and data checks, have steps that are well understood. Turning them into workflows with AI inside delivers most of the value, with less cost and risk than autonomous agents.

Anthropic's own advice points the same way: find "the simplest solution possible, and only increasing complexity when needed". Its engineers note that "the autonomous nature of agents means higher costs, and the potential for compounding errors". A small error early in an agent's run can be built on in every later step.

The market is learning this the hard way. According to Gartner's June 2025 prediction, over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls.

When is an autonomous agent worth it?

An agent is worth it when the task is open-ended, the number of steps cannot be predicted, and the value of handling that variety outweighs the extra cost and checking. Researching unfamiliar accounts, investigating issues across several systems and assembling bespoke analysis are typical examples.

Anthropic describes agents as suited to "open-ended problems where it's difficult or impossible to predict the required number of steps". Even then, the agent should read the business's knowledge, work within limited permissions, and stop for a person before anything goes out.

Agents are spreading, though unevenly. According to McKinsey's 2025 State of AI survey, 62% of organisations are at least experimenting with AI agents. Experimenting and relying on them are different things.

How do you design an agentic workflow?

Map the job as it is done today by your best person, mark which steps need judgement and which are mechanical, give AI the judgement steps with the knowledge they need, automate the mechanical steps, and put a person at the point where work goes out. Then measure it against how the job was done before.

A design checklist:

1. Write the method. The steps and checks your best person uses.
2. Mark the AI steps. Where reading, drafting or checking is needed.
3. Supply the knowledge. Product facts, voice, customers, rules.
4. Automate the rest. Data pulls, formatting, routing.
5. Place the approval. Before anything is published, sent or spent.
6. Measure. Time to approved output, and how much gets rewritten.

The guide to human in the loop covers placing the approval step.

Skein builds agentic workflows as the middle layer of a Company OS, each reading a shared company brain and marked with its approval point; the AI engineering page shows a workflow run end to end. That suits a business with frequent, well-understood jobs worth systemising. A team with genuinely unpredictable research work may benefit from a carefully scoped agent instead.

Frequently asked questions

What is an agentic workflow?

An agentic workflow is a set of predefined steps in which AI handles some of the work, such as reading, drafting or checking, while the overall path stays fixed. It is more predictable and cheaper than an autonomous agent, which makes it well suited to frequent business jobs like reports and proposals.

What is the difference between AI agents and agentic workflows?

In an agentic workflow, a person designs the steps and AI works inside them. In an AI agent, the AI decides its own steps towards a goal. Anthropic defines workflows as orchestrated through predefined code paths, and agents as directing their own process and tool use.

Should a small business use AI agents or workflows?

Start with workflows. The jobs that waste most time in a small business usually have known steps, so workflows with AI inside deliver most of the value at lower cost and risk. Consider agents only for genuinely open-ended tasks where the steps cannot be planned.

Why are AI agents expensive?

Agents choose their own steps, which often means more model calls, more tool use and more checking than a fixed workflow. Anthropic's engineers note that the autonomous nature of agents means higher costs and the potential for compounding errors, where an early mistake is built upon later.

Can an agentic workflow include a human?

Yes, and it should for anything that leaves the business. A good workflow lets AI draft, research and check freely, then hands the result to a named person who approves anything published, sent to a customer or that spends money.

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