How to Run a 30 Day Pilot Replacing One RPA Process with an Agent
Your finance team waits weeks for an invoice reconciliation bot that crashes whenever the ERP changes a button. Your ops team has a dozen bots that need rebuilding after every release. The backlog of broken bots is growing, and people keep saying, "just write better SOPs." But those SOPs still sit in a wiki because no one has time to build a bot for every one. The real problem is not a lack of process documentation. It is that the tool you are using, traditional RPA, cannot follow those SOPs reliably when the UI changes or an error occurs. A computer use agent can. Here is how to move from a single brittle bot to an agent-driven pilot in 30 days.
Why RPA breaks here
Most enterprise RPA today works by binding to a specific element on the screen: a selector, XPath, or object ID. When the application updates, those identifiers change and the bot stops working. Industry research shows that at least 40 percent of RPA maintenance hours go into fixing these kinds of issues. A single UI refresh can require a developer to locate the new selector, update the automation, test it, and redeploy. For high-volume, stable back-office tasks, this trade-off makes sense. But once you start automating processes that touch many different screens, rely on human decisions, or live in legacy or virtualized environments, the selector-based approach becomes a liability.
What changes with computer use agents
- ●Survives UI changes without rebuilding
- ●No brittle selectors to maintain
- ●Recovers from exceptions instead of halting
- ●Follows the SOP as written
- ●Works on legacy apps, Citrix, and virtualized desktops where RPA struggles
RPA wins at volume and stability. Computer use agents win where the UI changes, where exceptions happen, and where processes are documented in natural language.
How to move without the risk
You do not need to replace all your RPA at once. Start with one concrete, high-pain process. Pick a process that: (1) has a clear step-by-step SOP, (2) is error-prone, and (3) lives in an environment where RPA already struggles, such as a legacy ERP or a Citrix session. The goal is not to prove that agents are perfect. It is to demonstrate that agents can reliably follow that SOP and recover from exceptions that would otherwise halt a bot. A 30 day pilot gives you enough time to collect uptime, error rates, and operational data to make an informed decision about scaling.
Day 1, 3: Build the pilot plan
1. Define the process. Take your existing SOP and map each step to a concrete action: click a button, type a value, read a field, handle an error.2. Choose a small, bounded scope. For example, reconcile a single invoice type across three systems.3. Document the success criteria. Track uptime, error count, and how often the agent had to recover from an exception.4. Set up your pilot environment. Coasty offers cloud VMs, a desktop app, and a /v1 computer use API so you can launch an agent from your CI/CD pipeline or run it in isolation.5. Prepare fallbacks. Keep the manual process running in parallel so you can always fall back if needed.
Day 4, 21: Run the pilot
Give the agent access to the same systems the human uses. Let it follow the SOP exactly as written. When it hits an error, for example, a missing field or an unexpected popup, watch what happens. Traditional RPA usually halts and logs an error. Coasty agents see the screen, read the error message, and can choose a recovery action from a small set of options (retry, skip, escalate). Over two weeks, compare the error rates and recovery behavior of the agent against your manual baseline. You will likely see fewer total errors and more ability to handle edge cases that previously required human intervention.
Day 22, 30: Measure and decide
After 30 days, review the metrics. Look at: uptime, error rates, how often the agent required human intervention, and the time saved on each cycle. If the agent consistently meets your success criteria, treat it as a durable component of your automation stack. You can then expand it to similar processes or combine it with your existing RPA bots in a hybrid model. If you find limitations, document them honestly. This is the right way to learn where agents fit best, not where they replace everything at once.
A 30 day pilot is enough to see whether computer use agents can reliably follow your SOPs, recover from exceptions, and survive UI changes. Coasty agents control real desktops, browsers, and terminals, and they are designed to adapt rather than break. If you want to run a pilot that is concrete, low risk, and focused on real operational pain, talk to the Coasty team. Book a demo at https://cal.com/coasty/15min.