What people mean by "agent"
AI agents are the most talked-about term in the AI ecosystem right now, and it feels like every tool either has an "agentic" version or is built entirely around agents. That alone makes it hard to tell when something is just an AI tool versus an actual agent, but there's a bigger problem underneath it: a real misconception online about what agents even are.
Two kinds of tasks
Tasks handed to AI usually fall into one of two categories, and the split isn't really about how complicated the task is. Some tasks are very complex, with many steps before you get a result, but the process itself is well documented and always looks the same. In that case, an AI workflow is usually the better choice over a fully autonomous agent.
A workflow example: sorting holiday photos
Here's an example. Say we have a batch of holiday photos with three friends, Adam, Matthew, and Rob. We want to sort each photo into a folder named after whichever friend appears in it. Since we know what our friends look like, we wrote a short description of each of them in a markdown file for the model to use as context. Once the model has that, it can go through the photos one by one and decide where each belongs.
This works well because the process doesn't change if we get more photos from one of our friends' phones later. The model still compares each photo against the descriptions and moves the file accordingly. Real life raises a few questions, of course: what happens to photos with none of our friends in them, what about photos with multiple friends, how do we catch mistakes? These are valid concerns, but they can all be turned into an if-then decision tree.
What counts as a workflow
IBM defines an AI workflow as a structured series of actions that uses an LLM or other AI algorithm to automate, coordinate, and enhance a process, producing a repeatable outcome. We like this definition because it leaves room for agents too. The line between the two isn't as clean as it sounds — agents can be part of a workflow, since some steps or subtasks might be complex enough to need one.
When the same task needs an agent
Now a slightly harder version of the same example. We still have the holiday photos and the markdown file, but this time we're not sure we have descriptions for everyone, and the descriptions themselves might change over time. Some photos are blurry or don't match any description at all.
That kind of dynamic, changing data is usually a sign you need an agent rather than a workflow. Since agents are built to make decisions instead of strictly following a fixed process, the instructions you give them should be short and broad, so the agent isn't boxed into a routine that doesn't fit the situation. Something like this would work for a photo-classifying agent:
Goal: classify the pictures by profile and move them to the correct folders.
Match: use the descriptions from the markdown file to identify people in the photos.
Ambiguity: if a photo matches more than one description, copy it into all matching folders.
Unsure: if you're less than 75% confident, or the photo doesn't match any description, move it to /unknown.
Workflows vs. agents, in practice
Building an AI workflow still leans on classical programming: you write rules and cases to produce a predictable outcome. With an agent, you hand a good chunk of those decisions to the model and let it work out its own approach.
That difference points to one of the bigger shifts in how AI handles processes. A workflow is narrow by design, which makes it predictable but hard to extend — adding a new process usually means rebuilding most of it. An agent, on the other hand, can often take on new processes with barely any changes.
The same agent, a new task
Take an agent with access to your email and calendar, built to summarize your inbox 15 minutes before a meeting so you walk in with the context you need. That same agent could write and send meeting agendas to attendees with little more than a small prompt tweak.
The tradeoff
As models improve and tools get more specialized, building agents that handle multiple, even complex, processes will keep getting easier. But LLMs still make mistakes, and those mistakes can do real damage if an agent has broad access to tools without clearly defined limits.
Which one do you need?
Trying to figure out whether a process you want to automate needs a workflow or a full agent? Get in touch and we'll help you decide.