I Compared Every Major Computer Use Agent in 2025. Most of Them Are Embarrassingly Bad.

The Benchmark Numbers Are Damning (If You Actually Read Them)

OSWorld is the gold standard for measuring how well an AI agent can complete real tasks on a real computer. Not toy problems. Not trivia. Actual desktop workflows. When OpenAI launched its Computer-Using Agent (CUA) in January 2025, their own announcement bragged about a 38.1% success rate on OSWorld and called it 'state-of-the-art.' Let that sink in. They launched a product, held a press event, and the headline number was that it fails on 62% of tasks. The Hacker News thread on the launch was brutal, with developers pointing out that Claude at the time was scoring around 22% on the same benchmark, which means OpenAI beat a low bar and still couldn't crack 40%. Fast forward to late 2025 and Claude Sonnet 4.5 had climbed to 61.4% on OSWorld, which is genuinely better. But Anthropic's own community forums are a different story. Users on Reddit are calling out rate limits with no public documentation, throttling mid-session, and performance that swings wildly depending on the time of day. You can have the best model score on paper and still ship a product that frustrates people every single day. The benchmark and the real-world experience are two completely different conversations, and most AI companies hope you never notice the gap.

Why RPA Was Never the Answer Either

Before the computer use agent wave hit, enterprises spent the better part of a decade betting on RPA. UiPath, Automation Anywhere, Blue Prism. Billions of dollars and thousands of consultant hours building brittle bots that broke every time someone changed a button color on an internal app. UiPath's own blog in 2025 published a piece about their new 'Healing Agent' feature, which exists specifically because their automation failures were so common they needed a whole separate AI system just to patch the broken bots. Think about that. The solution to RPA failing is more AI on top of the RPA. It's turtles all the way down. The core problem with traditional RPA is that it's rule-based. It doesn't understand context. It follows a script, and the second the world deviates from the script, it falls apart. A computer use agent that actually understands what it's looking at, reads the screen the way a human would, and adapts on the fly is a fundamentally different category of tool. The problem is that most of the current crop of computer-using AI tools still haven't fully cracked that adaptation part.

What Every Major Computer Use Agent Gets Wrong

• ●OpenAI Operator (CUA): Launched at 38% OSWorld accuracy. Researchers at Partnership on AI found it was screenshotting text instead of reading it directly, causing OCR errors on basic tasks. It's since been folded into ChatGPT Agent, which independent reviewers called 'a big improvement but still not very useful for important tasks.'
• ●Anthropic Claude Computer Use: The model scores are genuinely impressive on paper, but the product experience is a mess. Rate limits with zero public documentation, usage throttling that kicks in mid-task, and a community forum that reads like a support group for frustrated developers.
• ●Microsoft Copilot Studio Computer Use: Still in preview as of late 2025. Preview means 'we're not ready but we needed to announce something.' Enterprise teams are being asked to build workflows on a foundation that hasn't shipped yet.
• ●Legacy RPA (UiPath, etc.): Requires dedicated developers to build and maintain. Breaks on UI changes. Needs a 'Healing Agent' add-on just to survive basic app updates. Average employee still wastes 4 hours and 38 minutes per week on duplicate tasks even in companies that have deployed RPA.
• ●Generic LLM API calls dressed up as agents: Half the 'AI automation' startups out there are just wrapping GPT-4 in a for-loop and calling it agentic. They can't control a real desktop. They can't handle multi-step workflows that require visual context. They're chatbots with a marketing rebrand.

The Real Cost of Getting This Wrong

Companies love to talk about automation ROI in the abstract. Let's get concrete. Manual data entry alone costs U.S. businesses $28,500 per employee per year, according to Parseur's 2025 report. More than half of those employees are experiencing burnout from the repetition. Workers waste a quarter of their entire work week on manual, repetitive tasks, and 69% of them believe automation would fix it. They're right. The problem isn't that automation doesn't work. The problem is that the tools being sold as automation are still failing at an embarrassing rate, and companies are paying twice: once for the broken tool and once for the human who has to clean up after it. A computer use agent that fails 60% of the time isn't saving you money. It's creating a new category of cleanup work. And when that agent also hits rate limits unpredictably, or requires a developer to babysit it, you've essentially built a more expensive version of the problem you were trying to solve.

Why Coasty Exists and Why the Benchmark Score Actually Matters Here

I don't usually lead with numbers, but 82% on OSWorld is the number that ends the conversation. Coasty is the highest-scoring computer use agent on the OSWorld benchmark, and it's not particularly close. When every competitor is still arguing about whether they've cracked 60%, sitting at 82% means you're operating in a different tier of reliability. But here's what I actually care about beyond the benchmark. Coasty controls real desktops, real browsers, and real terminals. Not API calls pretending to be computer use. Not a chatbot that fills out one form and calls it done. It handles the full visual context of a screen the way a human operator would, and it can run agent swarms for parallel execution, meaning you can scale tasks across multiple workflows simultaneously instead of waiting for one bot to finish before the next starts. There's a desktop app, cloud VMs, a free tier to actually try it, and BYOK support so you're not locked into someone else's API pricing. The reason I trust the 82% number is that OSWorld is adversarial. It's designed to trip up agents with ambiguous tasks, changing interfaces, and multi-step workflows where one wrong click cascades into failure. Scoring 82% there means the agent is genuinely understanding context, not just memorizing click patterns. That's the difference between a computer use agent and a slightly smarter macro recorder.