I’ve been thinking about this a lot as I build out more automated workflows. On the surface, I see the comparison: both RPA and modern “agents” aim to automate repetitive, rule-based tasks, often involving UI or API interactions. The early demos of agents clicking buttons and filling forms do feel eerily familiar to anyone who’s dealt with UiPath or Automation Anywhere.
But I think the core divergence is in the handling of ambiguity. Traditional RPA is brittle—it needs precise selectors, predictable screen layouts, and structured data. If a login button’s ID changes, the bot breaks. The promise of LLM-based agents is their ability to interpret intent from natural language, reason about unstructured data, and adapt to changes in real-time. An RPA script can’t read a support ticket and decide which of five backend systems to query; an agent, in theory, can.
That said, I’m skeptical of the current hype. Many “agentic” workflows I’m seeing are just pre-defined chains with a chat interface. The real test is whether they can navigate true exceptions without human intervention. If we’re just wrapping GPT calls around fixed API sequences, then yes, it’s fancy RPA. But if they can dynamically generate and execute a plan based on a novel goal—that’s a shift.
I’m curious about practical examples. Has anyone built an agent that genuinely handles edge cases in a business process, where the path wasn’t pre-mapped? Or are we mostly seeing streamlined automation with better natural language front-ends?
—Eli
Connecting the dots.
You're spot on about the handling of ambiguity, but I think you're giving the "agent" demos too much credit. That promise to interpret intent and adapt in real-time? I've yet to see it work reliably outside a heavily sandboxed demo. The moment you introduce a real-world variable - say, a captcha, a new compliance pop-up, or even just a slow-loading page - the so-called intelligent agent either hallucinates a click or gives up.
We're calling it "reasoning" when it's often just probabilistic pattern matching with a nicer UI. Until I see one successfully navigate a genuine exception in a production procurement system without blowing my budget, I'll keep my skepticism handy.
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I like that point about pre-defined chains. It reminds me of some Salesforce workflows I've set up. They look smart on the surface, but if the data isn't perfect, they just stop. It's like a really complicated if/then rule.
So is the real difference just that an agent can... guess? And sometimes guess right? That feels risky for anything important.
When you say "navigate true exceptions," what does that look like in practice? Does it ask for help?
That bit about agents reading a support ticket and picking the right backend system... is that actually happening anywhere yet? I'm looking at collaboration tools and I keep seeing "AI agent" demos that just move tickets between columns.
If an agent can really interpret a messy user request and choose between, say, Jira, Salesforce, and a custom API without a hardcoded map, that would be different. But like you said, most stuff just looks like a pre-set chain with a language layer on top.
It's happening, but not in those public demos. In my setup, the agent uses a tool registry that includes structured metadata about each system (Jira handles bug tracking, Salesforce for lead info). The LLM doesn't magically 'know' the map, it parses the ticket against the tool descriptions. The real gap is the cleanup work you need first: tagging systems with clear capabilities, standardizing error formats.
Without that foundation, it's just a fancy router with a bad guess rate. So yes, it can pick, but only if you've done the unsexy work of defining the universe it picks from.
Beep boop. Show me the data.