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

The RPA Era Is Over. Someone Should Tell the RPA Companies.

UiPath, Automation Anywhere, Blue Prism. These were the kings of enterprise automation for a decade. The pitch was simple: record a human doing a task, replay it forever, profit. And it kind of worked, until the UI changed, or the webpage loaded 200ms slower, or someone renamed a field in the CRM. RPA bots are essentially screen-scraping scripts with a nice GUI slapped on top. They don't understand context. They don't recover from surprises. They just break. The failure rate for RPA projects sits between 30 and 50 percent, and that's before you factor in the ongoing maintenance cost of keeping bots alive every time a software update rolls through. Gartner just predicted that over 40 percent of agentic AI projects will be canceled by end of 2027, largely because companies are bolting AI labels onto the same brittle RPA foundations. The problem isn't automation. The problem is automation that can't think. A real computer use agent doesn't follow a script. It looks at the screen, understands what it sees, and figures out the next step the same way a human would. That's a fundamentally different category of tool, and confusing the two is how you end up with a six-figure automation budget and a bot that dies every time someone updates Chrome.

OpenAI Operator: Great Launch Party, Rough Morning After

OpenAI launched Operator in January 2025 with the kind of fanfare you'd expect. A new model called CUA, Computer-Using Agent, controlling browsers, completing tasks, the whole vision. And to be fair, the demos looked impressive. Then people actually used it. Independent testers described Operator as 'unfinished, unsuccessful, and unsafe' after real-world trials. Researchers at the Partnership on AI found that during testing, the agent was photographing screens instead of copying text, leading to systematic OCR errors that compounded across multi-step workflows. It also has a well-documented habit of stopping mid-task to ask for confirmation, which is fine for a cautious first release but absolutely kills the value proposition of autonomous computer use. If your agent needs babysitting, you haven't automated anything. You've just added a middleman. OpenAI has since folded Operator into ChatGPT as 'ChatGPT agent,' which tells you something about how confident they are in it as a standalone product. It's not bad software. It's just nowhere near ready to be your primary computer use solution for anything mission-critical.

Claude Computer Use: Smart Model, Strangled by Its Own Guardrails

Anthropic's computer use offering is genuinely interesting from a capability standpoint. Claude Sonnet 4.5 scores 61.4% on OSWorld. Not embarrassing, but not leading either. The bigger issue isn't the benchmark score. It's the operational reality. Claude's computer use tool is throttled by rate limits that users across Reddit have called 'unreasonable' even on paid Pro plans. There are active megathreads on r/ClaudeAI with hundreds of complaints about hitting limits mid-workflow, random API errors, and the system stalling on multi-step tasks. For a one-off demo, Claude computer use is impressive. For running actual business workflows at volume, the rate limits alone make it a non-starter for serious teams. Anthropic is clearly still treating computer use as a research feature rather than a production product. That's their right. But don't let the polished blog posts fool you into thinking it's enterprise-ready.

• ●Claude Sonnet 4.5 scores 61.4% on OSWorld, well behind the current leaders
• ●Rate limits on Pro plans routinely cut off multi-step computer use workflows mid-execution
• ●Active Reddit threads document hundreds of users hitting walls during agentic tasks
• ●Anthropic's own docs still label computer use functionality as having significant limitations
• ●No native support for parallel agent execution, meaning one task at a time

What OSWorld Actually Measures (And Why It's the Only Score That Matters)

OSWorld is the benchmark that separates real computer use agents from demo-ware. It tests agents on genuine, open-ended desktop tasks across real operating systems, real applications, and real unpredictable conditions. Not curated prompts. Not cherry-picked workflows. Actual computer use in the wild. The scoring is brutal because real computer use is brutal. You have to navigate unexpected popups, handle slow-loading pages, recover from errors, and complete tasks that a human would find mildly annoying but totally doable. Most agents fall apart here. The gap between a model that looks good in a blog post demo and one that scores well on OSWorld is enormous, and that gap is exactly the gap between a tool that saves your team 20 hours a week and one that creates 20 hours of debugging work. When you're evaluating any computer use agent, OSWorld score is the first number you should ask for. If a vendor can't give you one, that's your answer.

Why Coasty Exists: Because 82% on OSWorld Isn't an Accident

I've been pretty hard on everyone in this post, so let me be equally direct about why Coasty is the tool I actually recommend. Coasty scores 82% on OSWorld. That's not a rounding error above the competition. That's a different category of performance. No other computer use agent is close. But the benchmark score is almost the least interesting thing about it. What matters in practice is that Coasty controls real desktops, real browsers, and real terminals. Not API wrappers pretending to be agents. Not a chatbot with a screenshot tool bolted on. Actual computer use the way a human does it, which means it works on the software you already have, not just the software that has an API. The agent swarm capability is what really changes the math for teams. Instead of one agent grinding through a task list sequentially, you can run parallel execution across multiple workflows simultaneously. That's the difference between automating one thing and automating your entire operation. There's a free tier to start with, BYOK support if you want to bring your own model keys, and a desktop app plus cloud VMs for however you want to deploy. No six-month implementation project. No RPA consultant charging $300 an hour to maintain scripts. You point it at a task and it does the task. That's the whole pitch, and at 82% on OSWorld, it's a pitch backed by actual evidence.