Automation Anywhere Is Bleeding You Dry. AI Computer Use Agents Don't Care About Your UI Changes.

The RPA Maintenance Tax Is Absolutely Brutal

Here's the number that should make any CFO furious: Forrester found that maintenance eats up roughly 60% of the total cost of an RPA deployment. You pay to build the bot. Then you pay more to keep it alive. Then you pay again when it dies anyway. Ernst and Young pegged RPA project failure rates at 50%. Half. And one analysis of enterprise deployments found that a single bot portfolio can rack up over 750,000 euros in maintenance costs over three years. For bots doing things a smart intern could do in an afternoon. The core problem is structural and it isn't going away. Traditional RPA tools like Automation Anywhere work by recording pixel coordinates, UI element selectors, and brittle XPath queries. The second a developer moves a button two pixels to the left, or a SaaS vendor refreshes their interface, the whole bot crashes and pages your IT team at 2am. This isn't a bug. It's the architecture. Rule-based scripts cannot reason. They can only follow the exact path they were trained on, and the real world doesn't stay still.

What Automation Anywhere Actually Costs (They Don't Make This Easy to Find)

• ●Enterprise bot licenses run $15,000 to $50,000+ per bot per year depending on the tier, and that's before implementation consulting fees
• ●Most enterprises need a dedicated RPA Center of Excellence team just to manage deployments, which adds 3-5 full-time headcount at $80K-$120K each
• ●Gartner's 2025 Magic Quadrant still names Automation Anywhere a leader, which is exactly the kind of award that costs money to win and means nothing to your ops team at 3am when the bot is down
• ●Automation Anywhere's own pricing page pushes you toward a sales call, not a price list, which tells you everything you need to know about what this costs
• ●The total 3-year cost of ownership for a mid-sized RPA deployment routinely exceeds $1 million when you add up licenses, implementation, and maintenance

AI Computer Use Agents Are Not Just 'Better RPA.' They're a Different Thing Entirely.

This is where people get confused, and the RPA vendors are very happy to keep you confused. Automation Anywhere has started slapping 'AI' all over their marketing. They acquired Aisera in late 2025. They're calling everything 'agentic.' But bolting a language model onto a brittle script runner doesn't make it an AI computer use agent. A real computer use agent doesn't use selectors or pixel maps. It sees the screen the same way you do, reasons about what it's looking at, and decides what to do next. If the UI changes, it adapts. If it hits an unexpected error message, it reads it and figures out a path forward. This is not a subtle difference. It's the difference between a GPS that recalculates when you miss a turn and a GPS that just keeps repeating 'turn left' into a wall. Anthropic's Claude and OpenAI's Operator both took swings at this. Claude Sonnet 4.5 hit 61.4% on OSWorld, the industry's standard benchmark for real-world computer tasks. That's not bad. But it's not good enough to trust with your actual business processes.

The OSWorld Benchmark Doesn't Lie, and the Scores Are Telling

OSWorld is the benchmark that actually matters for computer use AI. It tests agents on real, open-ended computer tasks across operating systems, browsers, and desktop apps. No hand-holding, no simplified environments. Just a real computer and a task. Claude Sonnet 4.5 scores 61.4%. Anthropic's newer Sonnet 4.6 pushed that further. OpenAI's Operator is in the same neighborhood. These are serious models from serious labs and they're clearing roughly 60% of tasks. That means 40% of the time, they fail or need human intervention. For a lot of enterprise workflows, that failure rate is still too high to run unsupervised. This is exactly why the benchmark leaderboard matters so much right now. The gap between a 61% agent and an 82% agent isn't 21 percentage points. In production, it's the difference between a tool you can actually trust and one that needs a babysitter, which puts you right back where you started with RPA. Coasty sits at 82% on OSWorld. That's not a rounding error over the competition. That's a different tier of reliability.

Why Coasty Exists and Why the Score Gap Actually Matters

Coasty was built specifically because the computer use problem is hard and most solutions are either too brittle (RPA) or not reliable enough for real work (early-gen AI agents). At 82% on OSWorld, Coasty is the highest-scoring computer use agent available right now. Not by a little. By enough that it changes what you can actually automate without a human in the loop. It controls real desktops, real browsers, and real terminals. Not API wrappers. Not simplified web-only environments. Actual computer use the way a person would do it. If your process lives in a legacy desktop app that has no API, Coasty handles it. If your workflow spans five different tools with no integration layer, Coasty handles it. You can run agent swarms for parallel execution, which means tasks that would take one bot hours can be parallelized across multiple agents simultaneously. There's a desktop app, cloud VMs, BYOK support if you want to bring your own model keys, and a free tier so you can actually test it before committing. Compare that to Automation Anywhere's 'call our sales team for a quote' model and the choice becomes pretty obvious. You're not buying a promise. You're buying a benchmark score and a tool that works on day one.