I’m often asked about the difference between AI agents and AI workflows. With the current excitement around building agentic systems, many teams are quick to assume agents are the way forward for every use case. But that’s not always the best approach.
In this piece, I’ll unpack the distinction between agents and workflows, explain when each is appropriate, and share a few guidelines I’ve found useful, particularly from Anthropic, on how to approach agent design thoughtfully.
An AI workflow is structured, deterministic, and predictable. It involves orchestrating LLMs and tools through predefined paths, essentially a set of coded instructions. Because the path is hardcoded, the output is more consistent and easier to control.
Workflows are well suited for clear, repeatable tasks. Examples include:
You can use workflows even when LLMs are involved, such as summarising content or classifying inputs, as long as the decision tree is relatively fixed. They’re cost-effective, debuggable, and ideal for production environments where consistency matters.
Agents, in contrast, are dynamic and autonomous. They don’t just follow instructions, they decide what to do. A well-designed agent plans its own sequence of actions, chooses the tools it needs, and reflects on whether it’s progressing toward the goal.
An agent might be given a high-level task, such as “Develop a prototype for an energy-efficient cooling system.” From there, it breaks the problem down, chooses its steps, and navigates its way through them, potentially asking for clarification when needed.
This flexibility makes agents powerful for open-ended or ambiguous tasks where the path to success isn’t known in advance. But it also introduces complexity, higher cost, and a greater risk of unpredictable outcomes.
In most business scenarios, workflows should be your default approach. They’re cost-effective, more predictable, and easier to debug. Just because you can build an agent doesn’t mean you should.
Here’s when a workflow is not just sufficient, but optimal:
A large number of so-called “agents” in use today are actually just complex workflows with some LLM-based reasoning steps. That’s not a bad thing - it’s often the most effective design. If the task doesn’t require autonomy or planning, workflows are the safer and more efficient option. Start there. Only add complexity when it’s justified by the problem.
Agents are powerful, but they should be used selectively. Their strength lies in ambiguity, complexity, and adaptability. But that same flexibility also brings higher costs, unpredictability, and engineering challenges.
You should only consider agents when the task meets these criteria:
That said, a true agent requires significant investment in design, training, and monitoring. You need to trust its decisions and you need the infrastructure to catch and correct issues.
A final point I always emphasise, especially when teams get excited about agent development, is the need to build with restraint and clarity. Don’t confuse complexity with progress. Anthropic summed it up well in three core principles:
In other words, build agents only when the problem truly calls for one, and be rigorous about why you're doing it.
With companies looking to integrate AI into their operations, it can be tempting to leap straight into building agents. But in many cases, a well-designed workflow offers greater reliability, lower cost, and faster time to value.
Agents can be powerful, but they also come with trade-offs. Before you build one, ask: Is this truly an open-ended problem? Do we need this level of autonomy? Can we trust the agent’s decisions?
Start with the simplest viable solution. Scale complexity only when needed. And design with clarity, whether you're building workflows, agents, or something in between.