Pharma and Life Sciences: Validated Workflows with AI Agents
In pharma and life sciences, a single change in an eCTD submission portal or an internal lab system can take down an RPA bot. Teams see bot failures pile up, maintenance tickets grow, and SOPs that should be run by anyone remain human-only because the process is too fragile for automation. The result is a growing backlog of tasks that feel automatable but stay manual.
Why RPA breaks here
Traditional RPA binds to specific selectors, XPaths, or object IDs. When a vendor updates their UI, releases a new version of a web portal, or reorders fields, the bot stops working. Organizations report that around 30 to 50 percent of RPA projects fail or hit severe maintenance issues within the first year. In regulated life sciences, the stakes are higher: a bot that halts on a validation error can stall a regulatory submission or trigger a compliance investigation. Beyond the initial build, each UI change forces developers to rebuild the bot. Teams estimate that maintenance can consume 40 to 60 percent of the original development effort over two years. In high-volume environments, this creates a treadmill of fixes that never ends, while manual tasks remain on the backlog because the cost of keeping bots working seems too high.
What changes with computer use agents
- ●Agents see the screen and act like a human: they move the mouse, click, type, and read the result. This means they adapt when a field label changes or a new button appears.
- ●No brittle selectors or object IDs are needed. When the UI updates, the agent continues to work without reengineering the bot.
- ●Agents recover from exceptions and unexpected states instead of halting. If a page loads slowly or an error message appears, the agent can retry or follow a predefined human-like recovery path.
- ●A standard operating procedure written in plain English is already almost a prompt. Computer use agents can follow it directly, with no flowchart bot to build and babysit.
- ●Agents run across any application, including legacy systems, virtual desktop infrastructures, and Citrix environments where traditional RPA struggles.
The durable winning argument: computer use agents see the screen like a human, so they survive UI changes and exception-heavy workflows, while traditional RPA needs a developer rebuild on every update.
How to move without the risk
A phased approach lets you validate workflows with AI agents without betting everything on day one. Start with one high-pain, SOP-driven process such as regulatory data entry, documentation extraction, or batch reporting. Map the current steps into clear, plain-English instructions. Deploy a small agent team on a cloud VM or desktop app to run the process and compare results with manual execution. Measure accuracy, speed, and rework. If the agent meets your quality thresholds, expand the scope to additional processes. Keep RPA for high-volume, stable backend tasks where deterministic controls are critical. Over time, the agent layer handles the long tail of changing UIs and exception-heavy workflows, while RPA supports the core, predictable volume. This approach lets you realize the benefits of agents, adaptability and resilience, while preserving the reliability of RPA for the parts of the business that need it.
The same SOPs that guide your teams can now be executed by AI agents that see the screen and adapt to changes. If you want to see how computer use agents can validate your pharmaceutical and life sciences workflows, book a demo with the Coasty team at https://cal.com/coasty/15min.