Every automation problem seems to get pointed at an AI agent lately. Need to move data between two systems? Agent. Need to check something and send an alert? Agent. Need to fill out the same form every day? Somehow, also an agent.
RPA — robotic process automation — didn't go anywhere. It's just less talked about right now, and a lot of tasks are still better suited to it than to a model making decisions at every step.
What RPA actually does
RPA bots follow a fixed set of steps. Click here, read this field, copy it there, repeat. They don't reason about what to do — they do exactly what they were configured to do, the same way, every time. This breakdown of how RPA works and where it's used covers the mechanics in more detail if you haven't worked with it before.
That rigidity is the whole point. A bot copying invoice numbers from one system to another doesn't need judgment. It needs to do the same five clicks correctly, every single time, without ever deciding to do something slightly different because the input looked a bit unusual.
What changes with an LLM agent
An agent can look at input that doesn't match a fixed pattern, decide what it means, and choose what to do next based on that. That's useful when the task genuinely varies — an incoming support ticket, a document with inconsistent formatting, a decision that depends on context.
It's also slower, less predictable, and harder to test than a script that does the same five steps every time. The actual difference between an agent and something like a chatbot or assistant comes down to how much deciding the system does on its own — and every bit of that deciding is a place where the output can vary in ways a fixed RPA bot never would.
When RPA is still the right call
If the task is the same steps every time, with input that doesn't meaningfully vary, RPA is usually simpler, cheaper to run, and far easier to debug when something breaks. You can step through exactly what it did and why.
A concrete example: moving new orders from a web form into an internal spreadsheet, formatted the same way every time. There's no decision being made — it's copy, format, paste, repeat. Running that through an LLM agent would add cost, latency, and a small but real chance the model interprets a field differently on a given run. A fixed script does the same job with none of that risk.
When it's worth adding a model into the loop
The line usually shows up around handling variation. If some of the incoming orders are malformed, or the form data needs interpretation before it can be filed correctly, that's a place where a model earns its keep — often for just that one judgment call, with the rest of the pipeline staying as fixed, scripted steps.
That's also usually a smaller, cheaper change than building a fully autonomous agent from scratch. A lot of what actually gets built ends up being closer to a scheduled workflow with one model call inside it than a standalone agent making every decision. If you're weighing that kind of setup against a no-code tool, this comparison of Zapier and GitHub Actions is a useful reference for where the actual complexity and cost tradeoffs sit.
The real question
It's not "RPA or AI agent" as a blanket choice. It's "does this specific step require judgment, or does it just require repetition." Most workflows are a mix of both, and the mistake is treating the whole thing as one or the other instead of splitting it apart step by step.
More on how these approaches compare in practice over at Procwire.
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