Apr 23, 2025 1:15:00 PM 13 min read

AI Agents Vs. AI Workflows: What's The Difference, And When Should You Use Each?

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.

Understanding the Core Difference

What Is an AI Workflow?

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:

Extracting data from invoices and feeding it into a form
Generating and translating marketing copy
Routing customer service queries through defined rules

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.

What Is an AI Agent?

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.

When to Choose Workflows Over Agents

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:

The task is predictable and clearly defined
If you can outline the process from end to end, there’s no need to delegate control. This includes tasks like extracting structured data from documents, content generation with predefined templates, or routing requests based on a set of conditions.
You need speed and scalability
Workflows can be quickly deployed, tested, and replicated. You don’t need complex memory or planning loops to get value out of LLMs in these contexts.
Cost and performance are critical
Agents often explore multiple paths, invoke tools repeatedly, and require persistent context, which consumes tokens and compute. Workflows minimise those costs by doing only what’s necessary.
You want easier debugging and monitoring
With a workflow, the logic is transparent. If something breaks, you can trace exactly where and why. Agents, on the other hand, might make opaque decisions that are harder to track.
The consequences of errors are high
When mistakes could lead to legal, financial, or reputational issues, predictable behaviour matters. Workflows allow for safeguards and human-in-the-loop approvals where needed.
You’re solving a common or repetitive task
A significant portion of enterprise use cases don’t require planning or reflection. You may only need one or two LLM calls within an existing deterministic system for summarisation, extraction, classification, or generation.

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.

When AI Agents Make Sense

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:

The problem is open-ended or underspecified
If you’re asking for something like “Develop a prototype for an energy-efficient cooling system,” you don’t know in advance what steps will be needed. An agent can reason through the ambiguity, plan a course of action, and iterate along the way.
The decision tree is too complex to hardcode
Some tasks require evaluating multiple unknowns, navigating edge cases, or adjusting dynamically based on intermediate results. Building a deterministic system for this would be time-consuming and brittle.
The value justifies the investment
Agents take longer to build, test, and trust. But if the task is high-impact, like scaling up coding support, accelerating research, or enabling autonomous troubleshooting, it might be worth the trade-off.
You have the infrastructure to manage them
Agents need persistent memory, tool integration, context management, and fallbacks. They also require safeguards to detect and respond to hallucinations or failure modes.
You can tolerate variability
Outputs may differ slightly from run to run. That’s part of the autonomy. If the task benefits from exploration, creativity, or adaptation, that variability might be an asset.
You’re ready to test and iterate extensively
Reliable agents don’t happen on the first try. You’ll need to tune prompts, adjust planning strategies, and introduce feedback mechanisms.

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.

Designing Agents Responsibly

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:

Don’t build agents for everything
Start simple. If a workflow does the job, use it.
Keep agents simple when you do build them
Overcomplication leads to fragility and confusion.
Think like your agents
Design with clarity around the goal, the steps needed, and the environment your agent operates in.

In other words, build agents only when the problem truly calls for one, and be rigorous about why you're doing it.

Final Thoughts

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.