Your Enterprise Is Bleeding $28,500 Per Employee on Manual Work. A Computer Use Agent Fixes That Today.

The Numbers Are Genuinely Embarrassing

Let's not be gentle about this. Employees spend 62% of their working hours on repetitive tasks, according to Clockify's 2025 research. Fifty-five billion hours are wasted globally every single year. For a mid-sized company of 100 employees, that's over 77,000 hours of productivity evaporating annually into copy-paste hell. And 56% of those employees report burnout specifically because of repetitive tasks. You're not just wasting money. You're burning out your best people over work that should have been automated two years ago. The kicker? 42% of companies abandoned most of their AI initiatives in 2025, up from just 17% in 2024. So they know they need to automate. They just keep picking the wrong tools and giving up when those tools fail them. The cycle is maddening.

Why RPA Was a Trap and Most Enterprises Still Haven't Escaped It

• ●Traditional RPA bots break the moment a UI changes, a button moves, or a vendor pushes an update. Ernst and Young puts the failure rate at 30-50% for SAP-connected bots alone.
• ●A three-year RPA deployment from a vendor like Automation Anywhere can run $750,000+ once you factor in licensing, maintenance, and the army of developers keeping bots alive.
• ●RPA has zero judgment. It does exactly what it's told, even when what it's told is wrong. One bad input, one changed field name, and the whole pipeline falls over silently.
• ●The average RPA project takes 6-18 months to deploy. A computer use agent can be pointed at the same workflow in an afternoon.
• ●UiPath, the RPA market leader, spent 2024 trying to rebrand itself as an 'AI automation platform' because they know the writing is on the wall. Their own customers are asking why they're paying millions for something that breaks when someone changes a font.
• ●Maintenance costs for RPA bots often exceed the original build cost within 18 months. IT teams end up spending more time babysitting bots than building new capabilities.

OpenAI Operator and Anthropic Computer Use Look Great in Demos. Then Reality Hits.

To be fair, the big labs deserve credit for making computer-using AI a real category. When Anthropic launched Claude's computer use feature and OpenAI shipped Operator (now folded into ChatGPT agent), they proved the concept was viable. But 'viable' and 'enterprise-ready' are very different things. Operator launched in January 2025 and has since been quietly absorbed into ChatGPT's agent mode. The reviews from early enterprise users were consistent: impressive for simple, linear tasks, shaky when workflows get complex, and not something you'd trust with a multi-step financial process without a human watching over its shoulder. Anthropic's computer use tool is similarly constrained. It's a feature inside a chat product, not a purpose-built computer use agent designed for enterprise scale. The OSWorld benchmark, which is the actual industry-standard test for how well AI agents navigate real computer tasks, tells the story clearly. Most of these models cluster in the 30-55% range. That means they fail nearly half the time on real-world computer tasks. You wouldn't hire a contractor who failed half their jobs. So why are enterprises betting workflows on these tools?

What 'Enterprise-Ready' Actually Means for a Computer Use Agent

The phrase gets thrown around constantly, but here's what it actually requires. An enterprise computer use agent needs to handle multi-step workflows across different applications without falling over when something unexpected happens. It needs to work on real desktops and real browsers, not just sanitized demo environments. It needs to run in parallel, because one agent handling one task at a time doesn't move the needle at scale. It needs to be auditable, so when something goes wrong, you can see exactly what the agent did and why. And it needs to actually succeed at the tasks you give it, not just attempt them. Most tools in this space fail on at least two of those requirements. The ones that fail on 'actually succeed' are the ones burning enterprise budgets and generating those 42% abandonment numbers. The benchmark that cuts through the marketing noise is OSWorld. It tests agents on 369 real computer tasks across operating systems, browsers, and applications. It's the closest thing the industry has to a standardized, honest measure of whether a computer use agent can do real work.

Why Coasty Is the Answer Enterprises Have Been Waiting For

I'm going to be straight with you because the numbers back it up. Coasty scores 82% on OSWorld. That's not a cherry-picked benchmark or a narrow test. That's the industry-standard evaluation for computer-using AI, and 82% is the highest score any computer use agent has posted. The next closest competitors aren't even in the same zip code. That gap matters enormously in enterprise contexts, because every percentage point of failure is a workflow that needs a human to catch it, a ticket that needs to be filed, a manager who has to explain to their VP why the automation 'kind of works.' Coasty controls real desktops, real browsers, and real terminals. Not API wrappers that pretend to interact with software. Actual computer use, the way a human operator would do it, but faster and without getting tired or distracted at 4pm on a Friday. The desktop app deploys quickly. Cloud VMs mean you're not limited by local hardware. Agent swarms let you run tasks in parallel, which is where the real enterprise ROI lives. When you're processing 500 invoices or updating 1,000 CRM records, you don't want one agent working through a queue. You want 50 agents finishing in the time one would take. There's a free tier to start, and BYOK support if your security team has opinions about API keys (they always do). For enterprises that have been burned by RPA maintenance bills and underwhelmed by half-finished AI demos, Coasty is what those tools were supposed to be.