# AI Agents vs Workflows: what's the difference?

# Introduction

My work as a software engineer and technical architect is centered around building and delivering software doing things like designing systems, collaborating with dev teams and helping move solutions from idea to implementation to operation. It's a highly technical role, but not necessarily at the frontier or state of the art AI

Still, over the past months I've been paying attention to what's happening in the AI space. AI agents have caught my attention and I've been studying and experimenting the topic on my own

To me, software engineering has always been about solving problems with tech, which means understanding the what, why and how. While learning about AI agents, I kept running into one question: what actually is an agent and how is it different than an AI workflow? In this blog post I go over this distinction in a practical way

# What is an AI workflow?

A workflow can be defined as a series of steps needed to complete a task. Adding "AI" to it generally just means an LLM is involved in at least one of those steps. A good and easy to understand example would be a support system ticket where tickets can be created and an AI workflow can summarize the ticket, check relevant documentation and draft a response.

![Flowchart of an AI workflow for support handling. The diagram shows a top-to-bottom linear sequence: “Support Ticket Arrives,” “LLM Summarizes Ticket,” “Search Docs / Knowledge base,” “LLM Drafts Response,” “(Optional) Human Review,” and “Final Output.” Arrows connect each step, emphasizing a fixed, predefined workflow.](https://cdn.hashnode.com/uploads/covers/5e95b544a12c7e6a1a6ab882/045652ea-70c7-4a31-bce9-fcec45dcf158.png align="center")

*High-level use case diagram for "support ticket" AI workflow sample. Own creation*

The important part here is that the sequence is fixed. The model adds intelligence/reasoning inside the steps, but it's not free to decide on the whole process

# What is an AI Agent?

An AI agent goes one step further than a workflow. Instead of following a fixed sequence of steps, it is given a goal and some ability (tools) to decide what actions to take.

In our previous example of the support ticket workflow, with an AI agent, the agent gets into a feedback loop where the LLM can use tools, search docs or ask user for more info. Each internal loop step inside the agent also checks if current "reasoning" is enough to resolve or if need to continue with the agentic loop

![Flowchart of an AI agent handling a support request. The process starts with “Support Ticket Arrives,” then “Agent receives goal,” followed by “Decide next action.” From there, the agent can choose among three actions: “Ask human,” “Call internal tool,” or “Search docs / knowledge base.” The results are then evaluated in “Evaluate findings,” followed by a decision point labeled “Enough to resolve?” If no, the flow loops back to “Decide next action.” If yes, it proceeds to “Final Output.” The diagram emphasizes dynamic decision-making and iteration rather than a fixed linear path.](https://cdn.hashnode.com/uploads/covers/5e95b544a12c7e6a1a6ab882/f9c85679-9bbc-4ba0-a359-3548025eb394.png align="center")

*High-level use case diagram for "support ticket" using an AI agent. Own creation*

The important difference here is that the agent can choose actions dynamically, like using a tool, inspecting results, and loop to refine the output and complete the task. That flexibility is what makes the agents powerful and interesting, but also significantly more complex than workflows

# Why people confuse them?

It's easy to confuse workflows and agents because both look similar on the surface. Both use LLMs, tools, APIs and so on.

A workflow may already look smart enough to feel like an agent. Also, most agents are not fully autonomous either as they require a human input to start.

So the distinction is not really about whether AI is involved, It is more about how much freedom an AI system has to decide next step

*   **Workflows**: Has a predefined path. The system is mostly developer-controlled
    
*   **Agent**: Has more of a dynamic path towards a goal. The system is More system-controlled
    

# Conclusion

In software engineering, we usually try to use the right tool for the right job. I think the same concept applies well here. Not every AI-powered system needs to be an agent.

An AI workflow is a good choice when the path is known in advance and you want something easier to understand, test and control.

An AI Agent makes more sense when the system needs freedom to decide what to do in order to reach a goal or objective