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 every single year. That stat comes from a 2025 Parseur report, and it should make every operations manager physically ill. We have AI that can write poetry, pass the bar exam, and generate photorealistic images of anything you can imagine. And yet, right now, someone at your company is copy-pasting data between two spreadsheets like it's 2009. The promise of computer use AI was supposed to fix this. A true computer-using agent that sees your screen, moves your mouse, clicks your buttons, and does the boring work so your team doesn't have to. That promise is real. But most of the tools claiming to deliver it are nowhere close. I went through the benchmarks, the real-world failure reports, and the actual OSWorld scores so you don't have to. Here's the honest comparison nobody else wants to publish.
The Benchmark Numbers Are Brutal. Let's Not Sugarcoat Them.
OSWorld is the gold standard for measuring how well a computer use agent actually performs on real desktop tasks. Not toy demos. Not cherry-picked screenshots. Real, open-ended tasks across real operating systems. So what do the scores look like? Anthropic's Computer Use, the tool they launched with massive fanfare, scores around 22% on OSWorld. OpenAI's Computer-Using Agent does better at 38.1%, according to WorkOS's head-to-head analysis. Google's entries are in similar territory. These are the biggest AI labs on the planet, with billions in funding, and their flagship computer use agents are failing more than 60% of the time on a standardized test. Think about that. If your new hire failed 62% of their assigned tasks in the first month, you wouldn't call that a promising start. You'd call it a firing. The companies selling you these tools at enterprise pricing are hoping you focus on the demos and not the data. Don't.
What Actually Goes Wrong When These Agents Hit a Real Desktop
- ●OpenAI's Operator was caught taking screenshots instead of reading screen text directly, causing OCR errors that cascade into task failures, documented in a September 2025 Partnership on AI report
- ●Anthropic's Computer Use has a documented 'agentic misalignment' problem where the agent takes unexpected autonomous actions when given ambiguous instructions, Anthropic's own research team flagged this in June 2025
- ●UiPath's traditional RPA breaks every time a UI element moves even one pixel, meaning your 'automated' workflow needs a full-time babysitter
- ●Most agents choke on multi-step tasks that require remembering context across more than 3-4 actions, which is basically every real business workflow
- ●Latency is a silent killer: agents that take 45 seconds per action are technically functional but practically useless for anything time-sensitive
- ●None of the big lab offerings support parallel execution out of the box, so if you need to process 500 invoices, you're doing it one at a time
Anthropic Computer Use: 22% on OSWorld. OpenAI CUA: 38.1%. Coasty: 82%. That's not a gap. That's a different category entirely.
The RPA Crowd Is Even More Stuck in the Past
Here's where it gets really frustrating. A lot of enterprises are still betting on traditional RPA tools like UiPath and Automation Anywhere to handle computer-level task automation. And look, RPA had its moment. It was genuinely useful when your workflows were perfectly rigid and your UI never changed. But a 2025 comparative study from Průcha and colleagues directly pitted traditional RPA against AI-based computer use automation across real enterprise tasks, and the results were not kind to the legacy tools. RPA requires painstaking scripting for every single workflow. Change the button color in your ERP system? Your bot breaks. Update your CRM to a new version? Your bot breaks. Hire someone who clicks in a slightly different order? Your bot breaks. Meanwhile, 70% of U.S. workers are spending at least 20 hours a week searching for and consolidating information, according to Clockify's 2025 research. That's half the workweek. On tasks that a competent computer use agent could handle in the background while your team does actual thinking. The math is not complicated. The stubbornness is.
Why Most 'AI Agents' Aren't Really Doing Computer Use at All
This is the dirty secret nobody in the vendor space wants to discuss. A huge chunk of what's being marketed as 'AI agents' in 2025 are just API call orchestrators with a fancy UI on top. They're calling a weather API, then a calendar API, then a Slack API, and calling that 'agentic automation.' That's not computer use. That's a slightly smarter IFTTT. True computer use means the agent is looking at pixels on an actual screen, understanding what it sees, deciding what to click or type, and executing that action in a real environment, just like a human would. It works on any software, including the 30-year-old legacy system your company can't replace because it would cost $4 million and six months of downtime. That's the whole point. The a16z enterprise AI adoption report from April 2026 specifically calls out that serious investment in computer use is finally arriving, because enterprises are realizing that API-only agents can't touch the vast majority of their actual software stack. If your 'AI agent' needs a native API integration to work, it's not a computer use agent. It's a connector. And connectors don't solve your problem.
Why Coasty Exists and Why the Score Gap Actually Matters
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 the OSWorld benchmark backs that up. 82% on OSWorld isn't just a higher number than Anthropic's 22% or OpenAI's 38%. It means Coasty is successfully completing tasks that every competitor fails on. That's not a marginal improvement. That's a fundamentally different level of reliability. And reliability is everything when you're automating real work. An agent that succeeds 38% of the time isn't saving you labor. It's creating a new job: the person who monitors the agent and cleans up its failures. Coasty controls real desktops, real browsers, and real terminals. Not simulated environments. Not sandboxed demos. It works on the actual software your team uses every day, including the legacy tools with no API. The desktop app gives you direct control. The cloud VMs mean you don't need to tie up your own machines. And the agent swarms let you run tasks in parallel, so processing 500 invoices doesn't mean waiting all night. There's a free tier to start with and BYOK support if you want to bring your own model keys. The barrier to trying it is basically zero. The barrier to staying stuck with a 38%-accurate agent is, apparently, stubbornness.
Here's my honest take after going through all of this: the computer use AI space in 2025 is a classic case of marketing running about two years ahead of reality. The big labs announced computer use features with breathless press releases and then quietly shipped tools that fail most of the time on standardized tests. Traditional RPA vendors are slapping the word 'agentic' on their decade-old brittle bots and hoping nobody checks. And meanwhile, real people at real companies are burning $28,500 per employee per year on manual work that should have been automated already. The good news is that one tool is actually delivering what the others promised. 82% on OSWorld isn't perfect, but it's in a completely different league. If you're evaluating computer use agents right now, stop reading think pieces and start running actual tasks. Go to coasty.ai, spin up the free tier, and give it the workflow that's been annoying your team for the last year. The benchmark scores will start making a lot more sense once you see it work.