Every AI Agent Platform Compared in 2026: Most Are Losing to a Spreadsheet

The Benchmark That Exposes Everyone: OSWorld

If someone's pitching you an AI agent platform and they can't tell you their OSWorld score, walk away. OSWorld is the gold standard for evaluating computer use AI. It runs 369 real tasks across actual desktop environments, including web browsers, terminals, and native apps. No shortcuts. No cherry-picked demos. You either complete the task or you don't. So what do the scores actually look like in 2026? GPT-5.3 Codex from OpenAI posted 64.7%. Claude Opus 4.6 from Anthropic hit 72.7%. Claude Sonnet 4.6 came in at 72.5%. Those are the big names, the ones with billion-dollar marketing budgets and breathless press coverage. Meanwhile, Coasty is sitting at 82% on OSWorld, which is not just the highest score among commercial computer use agents right now, it's not even close. That's nearly a 10-point gap over Anthropic's best model. In a benchmark where every percentage point represents hundreds of real-world tasks done correctly, 10 points is enormous. The leaderboard doesn't lie, even when the press releases do.

The RPA Graveyard: What 'Automation' Actually Cost You

• ●Traditional RPA licensing is only 25-30% of total cost of ownership. The rest is maintenance, broken bots, and developer time you didn't budget for.
• ●30-50% of RPA projects fail outright, according to industry data. Not 'underperform.' Fail.
• ●UiPath's own blog published a post about their 'Healing Agent' to fix UI automation's biggest challenges, which is a polite way of admitting that their bots break constantly when interfaces change.
• ●Manual data entry carries a 1-6% error rate. At scale, that means thousands of corrupted records per year, compliance exposure, and decisions made on bad data.
• ●56% of employees report burnout specifically from repetitive data tasks. You're paying people six figures to hate their jobs.
• ●A typical office worker still spends 10% of their entire working life on manual data entry in 2026. That's roughly 200 hours per year, per person, doing something a computer use agent can handle in the background.

OpenAI Operator and Anthropic Computer Use: Honest Takes

OpenAI Operator launched in early 2025 with serious hype, then got quietly folded into ChatGPT as 'ChatGPT agent' by mid-year. That rebrand tells you something. The Computer-Using Agent (CUA) model underneath it is capable, but Operator was trained to decline a surprisingly long list of tasks, which is a real problem when you need an agent that can actually operate in a complex enterprise environment without stopping every five minutes to ask permission. Anthropic's computer use tool is genuinely impressive from a research standpoint. Claude Opus 4.6 at 72.7% on OSWorld is real progress. But here's the thing: Anthropic is a research-first company selling API access. You're not getting a polished desktop agent with a real UI, cloud VMs, and parallel execution out of the box. You're getting a powerful model that you still have to build everything around yourself. That's not a product. That's a component. The difference matters enormously when you're trying to actually automate something by next quarter, not next year.

What 'Computer Use' Actually Means (And Why Most Tools Fake It)

Real computer use AI means the agent sees your screen, moves a cursor, clicks buttons, fills forms, reads outputs, and makes decisions, just like a human would, but faster and without complaining. It works on any software because it interacts with the UI, not an API. This is critical. API-based automation only works when the app you need has an API, when that API does what you need, and when someone has already built the integration. That's three big 'ifs.' Real computer-using AI has zero of those dependencies. It sees the screen. It acts. Done. The problem is that a lot of platforms marketing themselves as 'computer use agents' in 2026 are still heavily reliant on APIs and structured integrations under the hood, with a thin layer of LLM on top for the demo. They look great in a controlled walkthrough. They fall apart on your actual messy enterprise software stack. The OSWorld benchmark cuts through this because it tests agents on real, uncontrolled desktop environments. A 64% score means the agent fails more than a third of the time on standardized tasks. What do you think it does on your custom internal tools?

Why Coasty Exists

I'm not going to pretend I stumbled onto Coasty by accident. I was looking for a computer use agent that actually worked end-to-end without a team of engineers holding its hand, and the OSWorld leaderboard kept pointing to the same place. Coasty at 82% isn't just a benchmark flex. It reflects an architecture built specifically for real desktop control, not a general-purpose LLM with a computer use tool bolted on. The platform controls real desktops, browsers, and terminals. It runs on cloud VMs so you don't need to provision your own infrastructure. It supports agent swarms for parallel execution, which means if you have 50 tasks to run, you don't wait for them to queue up one by one. There's a free tier to actually try it before committing, and BYOK support if you want to bring your own model keys. What makes it different from Anthropic's computer use API or OpenAI's agent stack isn't just the score. It's that it's a complete product, not a research artifact. You can deploy it today on real workflows and it will not ask you to hire a developer to make it work. That's rarer than it should be in 2026.