I Tested Every Major Computer Use Agent in 2025. Most of Them Are a Joke.
Manual data entry is costing U.S. companies $28,500 per employee per year. That stat dropped in July 2025 and barely made a ripple, because everyone was too busy arguing about which AI agent was going to save them. Here's the problem: most of the computer use agents people are actually deploying can't even complete 40% of real-world tasks on a standardized benchmark. You're paying for hype, not performance. I've dug through the benchmarks, the launch announcements, the Reddit meltdowns, and the fine print. This is the comparison nobody in the AI press wants to write, because it makes too many powerful companies look bad.
The Benchmark That Exposes Everyone
OSWorld is the gold standard for testing computer use agents. It's not a cherry-picked demo. It's a real, standardized environment where agents have to complete actual desktop tasks, the kind of things your employees do every single day. So what do the scores look like? When OpenAI launched their Computer-Using Agent (CUA) in January 2025, they celebrated a 38.1% success rate on OSWorld like it was a moon landing. Anthropic's Computer Use feature scored 22%. Think about that for a second. The two most hyped, most funded AI labs on the planet built computer use agents that fail on more than 60% and 78% of tasks, respectively. If a human employee failed 62% of their assigned work, you'd fire them by lunch. These tools are being sold to enterprises as productivity multipliers. The math doesn't math. The honest truth is that most computer use agent launches in 2024 and early 2025 were capability demonstrations dressed up as products. Impressive in a 3-minute YouTube video. Painful in production.
Why RPA Was Always a Trap (And AI Agents Are Making the Same Mistakes)
Before we pile on the AI agent crowd, let's talk about the RPA era, because this story has a prequel. UiPath, Automation Anywhere, Blue Prism. These companies convinced enterprises to spend billions on bots that mimicked mouse clicks and keyboard inputs. The pitch was irresistible. The reality? Between 30% and 50% of RPA projects fail outright. The ones that survive require constant, expensive maintenance every time an app updates its UI, every time a button moves three pixels to the left, every time someone at Salesforce decides to redesign the dashboard. Analysts started calling these bots "brittle" and that's generous. They're made of glass. The hidden costs crushed the ROI promises. Consultants, platform fees, bot maintenance teams, emergency patches at 2am when a critical workflow breaks during month-end close. Companies didn't automate their work. They just created a new category of work: keeping the automation alive. Now look at what most first-generation computer use agents are doing. They're taking screenshots, clicking coordinates, and hoping the UI doesn't change. Sound familiar? The architecture is more sophisticated, sure. But if the failure modes are the same, you haven't actually solved the problem. You've just made it more expensive and harder to debug.
The Real Numbers Behind the Productivity Crisis
- ●$28,500 per employee per year lost to manual data entry tasks, according to Parseur's July 2025 report.
- ●56% of employees report burnout specifically from repetitive, manual data tasks. More than half your team is grinding themselves down on work a machine should be doing.
- ●30-50% of RPA implementations fail before they ever reach production, per industry analysis from 2025.
- ●OpenAI's CUA hit 38.1% on OSWorld at launch. Anthropic's Computer Use hit 22%. Both were announced as breakthroughs.
- ●Workers waste roughly a quarter of their work week on manual, repetitive tasks, per Smartsheet research. That's 10 hours a week, per person, every week.
- ●Claude Sonnet 4.5 reached 61.4% on OSWorld by late 2025, showing the gap between labs is closing fast for some, and widening for others.
- ●The computer use agent market is moving so fast that a tool that was competitive 6 months ago is already obsolete. Most buyers have no idea.
"$28,500 per employee per year. That's not a rounding error. That's a salary. You are paying one full human salary worth of waste, per person, just in manual data work. Every year. And the best-funded AI computer use agents on the market still can't complete 40% of tasks reliably."
Anthropic vs. OpenAI vs. The Rest: A Brutally Honest Breakdown
Anthropic's Computer Use launched in late 2024 with incredible fanfare. Claude controlling a real desktop, filling forms, navigating browsers. The demos were genuinely cool. The benchmark score of 22% on OSWorld was not. To Anthropic's credit, they've been iterating hard. Claude Sonnet 4.5 pushed the OSWorld score to 61.4% by September 2025, which is a real improvement. But here's the catch: that score is on OSWorld-Verified, a revised version of the benchmark, which makes direct comparisons to older scores messy. Convenient timing, some researchers noted. OpenAI's CUA came out in January 2025 with a 38.1% OSWorld score and a lot of confidence. By July 2025, they folded Operator into the broader ChatGPT agent product. The benchmarks improved, but the core limitation remains: these are web-centric agents. Get them off a browser and into a real desktop environment, a legacy enterprise app, a terminal, a complex multi-step workflow across three different tools, and the performance drops fast. Then there's the enterprise RPA crowd. UiPath is still out here charging premium prices for bot infrastructure that requires a dedicated maintenance team. Their answer to AI agents has been to bolt AI features onto the existing RPA architecture, which is a bit like putting a Tesla badge on a 2009 Camry. Google DeepMind sponsored computer use tracks at AgentX. Microsoft has Copilot doing computer use tasks inside Windows. Everyone is in this race. Most of them are still figuring out the fundamentals.
Why Coasty Exists and Why the Score Gap Actually Matters
I don't throw around benchmark numbers casually, so let me be direct about why 82% on OSWorld is a different category of result. Coasty.ai sits at 82% on OSWorld. For context: OpenAI's CUA launched at 38.1%. Claude Sonnet 4.5 reached 61.4% after months of iteration. Coasty is doing this on real desktop environments, not just browser tasks. It controls actual desktops, browsers, and terminals. It handles the messy, non-API stuff that every other tool quietly avoids in their demos. The architecture matters here. Coasty runs as a desktop app, spins up cloud VMs, and supports agent swarms for parallel execution. That last part is underrated. Most computer use agent tools are single-threaded by design. You give them a task, they do it, you wait. Swarms mean you can run multiple agents simultaneously across different workflows, which is how you actually move the needle on that $28,500 per employee number. It also supports BYOK (bring your own keys) and has a free tier, which means you can actually test it against your real workflows before committing. That's the thing about being confident in your benchmark score: you don't need to hide the product behind a sales call. The 82% number isn't marketing. It's the reason the product exists.
Here's my honest take after all of this research. The computer use agent space in 2025 is exactly where the smartphone market was in 2008. Most products are genuinely impressive compared to what existed two years ago. Most products are also genuinely not ready for the workflows companies are deploying them on. The benchmark scores tell the story that the press releases won't. A 22% success rate is not a product. A 38% success rate is a beta. An 82% success rate is the only number in this comparison that belongs in a production environment. If you're still running manual data workflows, still paying for brittle RPA bots that break every quarter, or still evaluating computer use agents based on demo videos instead of OSWorld scores, you're making a decision that's costing you real money right now. Go test Coasty. The free tier is there. The benchmark is public. The gap between it and every competitor in this comparison is not close. coasty.ai