UiPath Had a 10-Year Head Start. AI Computer Use Agents Lapped It Anyway.
Up to 50% of RPA projects fail. Not 5%. Not 15%. Half. Ernst and Young said it. Gartner confirmed the trend. And yet, for the better part of a decade, enterprises kept writing six-figure checks to UiPath, Automation Anywhere, and Blue Prism to build armies of fragile bots that break every time someone changes a button color in the UI. That's not automation. That's paying a lot of money to create a new maintenance problem. Meanwhile, a new class of computer use AI agents has arrived, and they don't need you to map every pixel, write every rule, or hire a dedicated bot-ops team. They just... do the work. The contrast is almost embarrassing.
The RPA Honeymoon Is Over. The Divorce Is Getting Ugly.
Let's talk about what's actually happening at UiPath right now, because it's not pretty. The company is currently facing a securities fraud class action lawsuit. The core allegation, according to court documents filed in September 2025, is that UiPath was internally aware it was failing to retain customers and hiding that reality from investors while forcing them onto a new 'Flex' platform. The stock got a price target cut to $14. Revenue guidance was slashed. Customers were leaving. This isn't a company on the rise. This is a company that built a moat around complexity, charged enterprises a fortune to maintain that complexity, and is now watching the moat drain. The broader RPA market tells the same story. A PwC survey found 30% of RPA projects failed to meet expectations outright. EY put first-project failure rates between 30% and 50%. ActiveBatch pegged overall RPA success rates at around 50%. You would not accept a 50% success rate from any other enterprise software. You'd demand a refund. But somehow, for years, RPA vendors convinced IT departments that this was just the cost of doing business.
Why RPA Breaks (And Why It Will Always Break)
- ●RPA bots are coordinate-based. They click pixels at fixed screen positions. Change the UI, move a button, update a form field, and the bot falls over. Every software update is a potential incident.
- ●Maintenance costs eat the ROI alive. LinkedIn automation consultants are openly advertising how to 'cut RPA maintenance costs by 65%', which tells you everything about how bad the baseline is.
- ●RPA has zero judgment. It follows a flowchart. It cannot handle exceptions, ambiguity, or anything that wasn't explicitly programmed. Real work is full of exceptions.
- ●Scaling RPA means buying more bot licenses, hiring more bot developers, and managing more infrastructure. The cost curve goes up, not down.
- ●A 2025 blog post from a major RPA vendor literally described RPA bots as 'dumb bots that don't cope with variability.' That's a vendor describing their own category. Let that sink in.
- ●Gartner predicts over 40% of agentic AI projects will be canceled by 2027, but their specific reason is telling: companies are trying to bolt 'agentic' labels onto old RPA and chatbot infrastructure. The problem isn't AI agents. It's legacy RPA pretending to be AI agents.
EY found that 30% to 50% of first RPA projects fail outright. UiPath's own customers were churning hard enough that the company now faces a securities fraud lawsuit over allegedly hiding it from investors. This is the automation platform that enterprises spent a decade betting on.
What a Real Computer Use Agent Actually Does Differently
Here's the fundamental difference, and it's not subtle. RPA sees a screen and executes a hardcoded script. A computer use AI agent sees a screen and understands it, the same way a person does. It reads the interface, reasons about the goal, and figures out the steps. You don't define the path. You define the outcome. That's a completely different category of tool. A computer use agent can navigate a web app it's never seen before. It can handle a popup that wasn't there yesterday. It can read an error message, understand what it means, and adapt. It works across browsers, desktop apps, and terminals without needing a custom integration for each one. No API required. No pixel-mapping required. No bot developer required. The benchmark that separates real computer use AI from marketing noise is OSWorld, a rigorous test of how well an AI agent handles real-world computer tasks across different operating systems and applications. Most models cluster in the 30-60% range. The gap between a 40% score and an 82% score in real-world usage is the difference between a tool that kind of works sometimes and one you can actually run your business on.
UiPath Is Trying to Pivot. It's Not Going Well.
To be fair to UiPath, they see the writing on the wall. Their fiscal year 2025 annual report is full of language about 'agentic automation' and 'AI agents.' They launched something called Agent Builder. They're using all the right words. But there's a massive difference between a legacy RPA company slapping 'agentic' on their existing product and a platform that was built from the ground up to control computers the way a human does. UiPath's core architecture is still built around structured automation flows and bot orchestration. Wrapping that in an AI layer doesn't change what's underneath. It's like putting a Tesla badge on a 2009 Prius. The Reddit community in r/rpa is openly debating whether UiPath's AI additions are real or just repositioning. One commenter put it plainly: 'They are adapting and introducing AI Agents,' which sounds positive until you realize 'adapting' is a polite word for 'scrambling.' Meanwhile, companies that built computer use AI agents from scratch, without the legacy baggage, are running laps on every meaningful benchmark.
Why Coasty Exists
I'm not going to pretend I don't have a dog in this fight. I think Coasty is the best computer use agent available right now, and I can back that up with a number: 82% on OSWorld. That's not a marketing claim. OSWorld is the industry-standard benchmark for computer-using AI, and 82% is the highest score on the board. No other agent is close. Coasty controls real desktops, real browsers, and real terminals. Not API wrappers. Not RPA scripts dressed up in AI clothing. Actual computer use, the kind where you point it at a task and it figures out how to do it across whatever applications are involved. You can run it as a desktop app, spin up cloud VMs, or deploy agent swarms for parallel execution when you need to move fast at scale. There's a free tier if you want to test it before committing. BYOK support if you have your own model keys. The whole thing is designed for people who are done paying UiPath-sized licensing fees for bots that break when someone updates the CRM. If you've been burned by RPA before, the architecture here is genuinely different. That's not a sales pitch. That's just what the benchmark data shows.
Here's my honest take. RPA was a reasonable solution in 2015, when AI couldn't actually see and understand a screen. That world is gone. We now have computer use AI agents that score 82% on the hardest real-world benchmarks, that don't need pixel-mapping or bot developers, and that adapt when the UI changes. Sticking with UiPath in 2026 because you already have a license is like keeping your fax machine because you already paid for the paper. The sunk cost isn't a reason to stay. It's a reason to do the math on what you've already lost. If you want to see what a modern computer use agent actually looks like in practice, go to coasty.ai. The free tier is right there. Take an hour. Run a real task. Then decide whether your current setup is still worth defending.