How to Run a 30-Day Pilot Replacing One RPA Process with an Agent

Why RPA breaks here

Traditional RPA relies on selectors, XPath, and object IDs to locate controls. When a UI changes, the selector becomes invalid. The bot halts. Your team must rebuild it. Industry surveys show that about 30 percent of RPA maintenance effort goes into fixing selector or object resolution issues. Each rebuild can take hours. For a midsize enterprise with 50 bots, that can mean hundreds of hours per year just to keep existing bots running. Beyond the direct time cost, there is the hidden cost of stalled projects. A change that is supposed to save 30 minutes per run can become a three-day rebuild effort, pushing the ROI timeline out by months. In many cases, the process never gets a proper automation because the maintenance overhead outweighs the savings.

What changes with computer use agents

• Agents see the screen and act like humans: they move the mouse, click, type, and read the result. They do not depend on brittle selectors to locate buttons or inputs.
• When the UI changes, agents adapt. They find the new location of the button or input field, so the process continues without a rebuild.
• Agents recover from exceptions. If a form fails validation or an unexpected modal appears, they can read the screen, reason about the state, and take a next step instead of halting.
• Agents follow SOPs written in plain English. A step-by-step procedure is already a prompt. The agent can execute it directly, without building a flowchart bot.
• Agents work across any application, including legacy systems, Citrix, and virtualized desktops where traditional RPA struggles because it cannot see the screen content.

How to move without the risk

You do not need to rip out all RPA at once. A 30-day pilot focused on one high-pain process can reveal the difference and build confidence. Choose a process that has frequent UI changes, many exceptions, or that sits on legacy or virtualized platforms. The goal is not to prove that agents are perfect but to show they can handle the real-world variability that breaks traditional bots. Start by writing a current SOP in plain English. Document each step: open the app, fill out the form, verify the result, close the window. This is the prompt the agent will follow. Next, set up a pilot environment. You can run Coasty agents in cloud VMs, a desktop app, or via the /v1 computer use API. Test the agent on the pilot process and compare uptime, error rates, and time saved against the existing RPA bot. Measure how often the agent adapts to UI changes without developer intervention. Track how many exceptions it resolves on its own. After 30 days, you will have concrete data on maintenance hours, error frequency, and the total cost of ownership. This evidence can justify expanding the pilot to more processes and eventually blending agents with traditional RPA where it still makes sense. Remember, traditional RPA still fits very high volume, stable, deterministic, backend tasks. The win for computer use agents is the long tail, changing UIs, exception-heavy work, and SOP-driven processes.

What you should measure in 30 days

Define clear metrics before you start. Track the number of bot failures per week, the average time to fix each failure, and the cumulative maintenance hours per month. Compare the error rate and time to resolution for the existing RPA bot versus the agent pilot. Note how often the agent encounters a new UI change and how quickly it adapts without developer involvement. These numbers will show whether the pilot reduces total cost of ownership and frees developers for higher-value work.