I Tested Every Major Computer Use Agent in 2026. Most of Them Are a Joke.

The Benchmark Scores Are Damning. Let's Actually Read Them.

OSWorld is the industry-standard benchmark for AI computer use. It throws real-world computer tasks at agents and measures whether they actually complete them. Not demo tasks. Not cherry-picked examples. Real stuff. Here's what the leaderboard looks like for the big names everyone keeps hyping. Anthropic's Computer Use, the one that got a splashy launch and a ton of press, scores around 22% on OSWorld. OpenAI's Computer-Using Agent (CUA), which powers Operator, does better at 38.1%. Claude Sonnet 4.5 pushed that number to 61.4%. Those are real numbers from real evaluations. Now think about what they mean. At 22%, Anthropic's original Computer Use agent fails at nearly 4 out of every 5 tasks. At 38%, OpenAI's CUA is failing at almost 2 out of 3. You wouldn't hire a contractor who only finished 38% of their jobs. You definitely wouldn't pay enterprise pricing for one. And yet here we are, with major companies betting their automation roadmaps on tools that, by objective measurement, can't complete most of what you throw at them.

OpenAI Operator: The Daily Limit Drama Nobody Warned You About

Let's talk about Operator specifically, because it's the one getting the most marketing dollars behind it right now. OpenAI launched it in January 2025 with considerable fanfare. What they mentioned less prominently was the daily usage limits that users started hitting within 12 hours of trying to actually use it for real work. Reddit threads from early 2025 are full of people describing the same experience: Operator fails a task, burns through your daily limit doing it, and you're stuck until the next day. The AI digest's review of current computer use agents put it plainly: current agents are still fairly unreliable and slow. That's not a fringe opinion. That's the consensus from people who've actually tried to deploy these things in production environments. Operator also has a documented tendency to decline tasks it deems sensitive, which sounds reasonable until the thing starts refusing to fill out routine web forms because it can't tell the difference between a legitimate workflow and something sketchy. You can't run a business on an agent that second-guesses every third action.

RPA Was Supposed to Fix This. It Didn't.

• ●Traditional RPA tools like UiPath require dedicated developers to write and maintain scripts for every single workflow, meaning you're trading one expensive problem for another expensive problem
• ●RPA breaks the moment a UI changes. A button moves 10 pixels to the left and your entire automation falls apart. Someone has to fix it manually. That someone costs money.
• ●McKinsey found workers spend half their time on activities that could theoretically be automated. The word 'theoretically' is doing a lot of heavy lifting there.
• ●Smartsheet research found workers waste a full quarter of their work week on manual repetitive tasks. That's 10 hours per person per week, every week, at every company still running legacy automation.
• ●The promise of RPA was 'set it and forget it.' The reality is 'set it, watch it break, pay someone to fix it, repeat forever.'
• ●AI computer use agents were supposed to be the answer to brittle RPA. Most of them just introduced new failure modes instead of eliminating the old ones.

The Manus Hype Cycle Was Embarrassing

Remember when Manus AI dropped in early 2025 and the internet collectively lost its mind? Viral demos. Waiting lists. People calling it the most impressive AI agent they'd ever seen. Then early access rolled out and the UX Collective's honest review landed like a bucket of cold water. The gap between the demo and the product was real, and users noticed fast. This is the pattern that keeps repeating in the computer use agent space. Someone ships a slick demo video. Twitter goes nuclear. Benchmarks get published. Reality sets in. The problem isn't that these teams aren't smart. They clearly are. The problem is that controlling a real desktop, in real time, across unpredictable applications and edge cases, is genuinely hard. Harder than most of these companies publicly admitted when they were collecting the hype. Demos are easy. Reliability at scale is not.

Why Coasty Exists and Why the 82% Number Actually Matters

I'm going to be straight with you. Coasty is the tool I'd point anyone to right now if they're serious about AI computer use, and the reason is a single number: 82% on OSWorld. That's not a marketing claim. OSWorld is the benchmark the whole industry uses, and 82% puts Coasty ahead of every competitor I've mentioned in this post. Anthropic at 22%. OpenAI CUA at 38%. Claude 4.5 at 61.4%. Coasty at 82%. The gap is not close. What makes that number meaningful in practice is what Coasty actually does. It controls real desktops, real browsers, and real terminals. Not API wrappers. Not sandboxed toy environments. The actual screen, the actual cursor, the actual keyboard inputs that get real work done. 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. There's a free tier if you want to test it before committing, and BYOK support if you already have model API access and don't want to pay twice. The thing that gets me about Coasty is that it was built by people who clearly got frustrated with the same nonsense I've been describing in this post. The 22% benchmark scores. The brittle RPA scripts. The agents that fail and then hit your daily limit. They built the thing they wanted to exist. That tends to produce better software.