APA vs. RPA: How AI Changes Process Automation

Traditional robotic process automation (RPA) is the old way to automate work: you define the steps and then you have a bot repeat them. It works well when systems is predictable but when a website changes its layout or adds a new form step, that automation often breaks.

Agentic process automation (APA) is the new approach. AI agents can interpret what they encounter and adjust their actions to complete a task. That adaptability helps them handle changes that would require someone to repair a traditional bot.

I’m Suchintan Singh, co-founder and CEO of Skyvern. We built Skyvern to bring this approach to browser automation. In this article, I compare APA and RPA, explain when you should choose each, and show how our clients use APA in production.

What is RPA, and why can it break?

Robotic process automation uses software bots to perform repetitive tasks through application interfaces and APIs. Teams define the actions, conditions, and data mappings the bot should follow.

This approach became established before today's generative AI agents. For predictable work, it remains useful: open an application, copy a value, fill a field, and repeat.

The weakness appears when the environment stops matching the workflow's assumptions. In a browser, a selector identifies an element such as a button or input. If the attributes it relies on change, the bot may no longer find its target.

An unexpected form step can create the same problem. Someone must add the logic needed to handle it. Retrying helps with a temporary interruption, but it does not repair a selector pointing to an element that no longer exists.

What is APA, and how does AI make it adaptable?

Agentic process automation uses AI agents to work toward a goal. An agent can inspect its environment, decide what action to take, and use the result to determine its next step. IBM describes this ability to adapt actions to changing environments and goals as a defining feature of agentic automation.

Take the invoice workflow. You might tell an agent: “Download last month's invoices from this vendor portal and return the files.”

If the billing section moves, an agent can examine the current navigation and find another route. If the download requires an additional selection, it can interpret the available options using the task instructions. The objective stays the same while the actions can change.

This is why APA can handle changes that would break a fixed workflow. It reduces dependence on someone anticipating every page layout and interaction beforehand. Our guide to agentic automation explains the approach in more detail.

APA vs. RPA at a glance

Evaluate both on completed work: whether the right document was retrieved, the correct data was entered, and the result reached the next system.

When should you choose APA over RPA?

Choose APA when your workflow spans external websites, interfaces change frequently, or the next step depends on what appears on screen. Its ability to reassess the page reduces the need to write and maintain rules for every variation.

Traditional RPA is still a good choice for stable, repetitive work with predictable inputs. It is still prone to break when something changes but if nothing ever changes, then it might just be good enough.

Before scaling, run a pilot with representative tasks, changed layouts, and missing information. Measure correct completion, how often someone needs to intervene, and cost per successful task.

For either approach, validate important outputs and compare exact values programmatically where appropriate. Include human approval before actions that require review.

How we're applying APA at Skyvern

We're building Skyvern around the ability to operate the browser from instructions while adapting to the page it encounters.

The agent can inspect page HTML, take screenshots, and perform browser actions. In our latest agent architecture, it chooses when to gather more information. That lets it investigate the current page as the task progresses.

We also support reusable workflows and code caching. Successful actions can become executable code for subsequent runs. If a cached run encounters a page change that causes it to fail, Skyvern can fall back to the agent and regenerate the code.

So we have the best of both worlds! Predictable steps benefit from efficient execution, while unfamiliar situations benefit from reasoning.

Teams can build visually or integrate through our APIs and SDKs, then inspect runs and their outputs. The aim is to reduce the manual work involved in both executing a process and keeping its automation useful.

How are our clients using APA in production?

Our clients use Skyvern for the browser work behind everyday operations. We've shared production examples including invoice collection and government filings, alongside client uses in healthcare and insurance.

For finance and accounts payable, our clients automate logging into vendor portals and collecting invoices and statements. Each portal can organize billing documents differently. APA helps navigate those differences while keeping the requested account and document criteria explicit.

In healthcare, our clients use Skyvern for explanation of benefits (EOB) processing and provider credentialing. These processes involve repetitive portal interactions, forms, and documents.

Our insurance clients use Skyvern for form filling within claims processing. The practical value is getting supplied information into the right fields as forms vary.

If your team has a recurring portal workflow to automate, start with one process in Skyvern's AI browser automation platform. Test it against the sites and exceptions your team encounters, then measure how much work it completes correctly and how often it needs help.