Your Enterprise Is Bleeding Money on Manual Work While AI Computer Use Agents Sit Unused

The RPA Dream Died and Nobody Sent the Memo

Remember when RPA was going to fix everything? UiPath, Automation Anywhere, Blue Prism, all promising that bots would handle the boring stuff. Enterprises spent billions. Ernst and Young pegged the RPA project failure rate at 50%. Forrester found that 60% of RPA deployments become a maintenance nightmare, with teams spending more time keeping the bots alive than the bots actually save. Traditional RPA tools break every time a website updates, a UI shifts, or a vendor changes their portal layout. And they break silently, which is the worst kind of break. You find out three weeks later when a report is wrong and someone has to manually reconstruct a month of data. The dirty secret of enterprise RPA is that it didn't eliminate manual work. It just moved it upstream to a team of RPA developers who spend their days fixing fragile scripts. That's not automation. That's a more expensive version of the problem you started with.

What Enterprises Actually Need (And Why They Keep Buying the Wrong Thing)

• ●62% of employee time goes to repetitive tasks, that's roughly $10.9 trillion in unproductive work lost annually in the US alone according to Clockify research
• ●Traditional RPA requires 20-30% of its total cost just for annual maintenance, before you've automated a single new workflow
• ●Gartner predicts 40% of agentic AI projects will be cancelled by end of 2027, mostly because teams deploy tools that can't handle real-world enterprise complexity
• ●OpenAI's Operator and Anthropic's computer use are still in limited research previews, not production-ready enterprise tools
• ●A real computer use agent operates at the UI layer, meaning it works on ANY application, legacy or modern, with no API integration required
• ●Agent swarms running in parallel can compress a 40-hour manual process into a fraction of the time, not by working faster but by working simultaneously

Anthropic and OpenAI Are Not Your Enterprise Solution

Let's be honest about what's happening in the market right now. Anthropic's computer use capability is genuinely impressive in a demo. OpenAI's Operator made a lot of noise when it launched in January 2025. Both are still explicitly research previews, not generally available enterprise products. One writer tested Operator and Anthropic's computer-use agent on a basic grocery ordering task and found both struggled badly with real-world web complexity. That's a grocery order. Imagine trusting either of these tools with your ERP, your compliance workflows, or your customer data pipelines. Beyond reliability, neither product is built for enterprise deployment at scale. No agent swarms. No fleet management. No serious security model for operating inside a corporate network. They're proof of concepts dressed up in product clothing, and enterprises are making six-figure commitments based on demos that don't reflect production reality. The AI computer use space is moving fast, but moving fast in the wrong direction with the wrong tool is worse than standing still.

What a Production-Grade Computer Use Agent Actually Looks Like

Here's the thing most vendors won't tell you. The hard part of computer use isn't understanding a screenshot. It's reliably executing a 47-step workflow across three applications, recovering gracefully when step 23 fails, logging what happened, and doing all of that 200 times in parallel without human supervision. That's what separates a research demo from an enterprise tool. A real computer use agent for enterprise needs to control actual desktops and browsers, not just call APIs that happen to have web interfaces. It needs to run in cloud VMs so your IT team isn't managing local installs across a thousand machines. It needs agent swarms so you can parallelize work that would otherwise be strictly sequential. And it needs to score well on objective benchmarks, not just look good in a carefully staged demo. OSWorld is the benchmark that actually measures this. It tests AI agents on real, open-ended computer tasks across real operating system environments. It's the closest thing the industry has to an honest stress test for computer-using AI.

Why Coasty Exists and Why the Benchmark Score Actually Matters

Coasty was built specifically for this gap. Not for consumers ordering pizza, not as a research toy, but as a production computer use agent for teams that need reliable, scalable automation on real enterprise workflows. The score on OSWorld is 82%. That's not a marketing claim, it's a measurable result on an independent benchmark, and it's higher than every competitor in the space right now. That gap matters more than it sounds. The difference between a 60% success rate and an 82% success rate on enterprise workflows isn't 22 percentage points. It's the difference between a tool your team trusts and one that creates more cleanup work than it saves. Coasty runs on a desktop app and cloud VMs, supports agent swarms for parallel execution, has a free tier so you can actually test it before committing, and supports BYOK so your data doesn't have to leave your control. It works on any application your team uses, whether that's a modern SaaS tool with a clean API or a 15-year-old internal portal that nobody has touched since the Obama administration. No custom integration required. No RPA developer needed. You describe the task, the computer use agent handles it. That's the actual pitch, and it's backed by the number.