Disclaimer: Written By Human. Proof reading by AI. Image by AI
Introduction
UI automation has been always a challenging task in our software development life cycle. t’s often time-consuming, brittle, and requires constant updates as platforms evolve. The platform itself keep changing over the period of time. You need to upgrade your QA resources with latest platform, write automation, re-write automation and upgrade automation. In large projects, this becomes a significant burden—especially given the heavy investment required to automate Web UI testing. During project development lifecycle there is a significant investment in automating the Web UI testing scenarios.
The entire process of Web UI Automation is consist of four steps.
- Define scenario to test
- Identify the control on UI to handle the scenarios
- Select these controls as part your automation (Either through screen recorder or through code)
- Perform Operation (Keep the sample data to operate on)
Problem
Step #1 and Step #4 are the value add. Remaining steps are mostly tool/platform specific which requires good amount investment to write it and over the period to maintain it.
Maintenance is a major challenge—features evolve rapidly, and automation scripts often lag behind, resulting in quality issues and delayed releases. Only the most mature teams are able to release multiple times a week without automation bottlenecks.
Opportunities with AI
With AI coming, the landscape is changing. There are new tools which has come to automate your entire Web interaction with just prompt – no need for brittle selectors or complex scripts. This looks incredibly promising as it can revolutionize our UI automation need. I explored few tools and they look quite sleek. Below are few tools and what they can offer for automation. These tools are worth experimenting.
| Feature / Tool | Browser-Use | Stagehand | Notte |
| Type | SDK / CLI / UI | JS SDK (Playwright) | Python SDK + cloud |
| LLM Support | Multi-LLM | Generic LLMs | LLMs + structured browsing |
| Platform | Python + self-hostable UI | Node.js | Python + optional cloud |
| Ease of Setup | Moderate | Dev-centric | Moderate–Complex |
| Privacy | Self-hosted option | Depends on usage | Secure vault for creds |
| Robustness | Good | Self-healing | High with perception layer |
| Best Use Case | Web scraping, form automation | Code + AI hybrid automation | Scalable production agents |
Code Sample with Browser-Use
import asyncio
from dotenv import load_dotenv
load_dotenv()
from browser_use import Agent
from browser_use.llm import ChatOpenAI
async def main():
print("Hello from browseragent!")
agent = Agent(
llm=ChatOpenAI(model="gpt-4o-mini", temperature=0),
verbose=True,
task="search for the best pizza in new york",
)
result = await agent.run()
print(result)
# Add Main function here
if __name__ == "__main__":
asyncio.run(main())Ofcourse you need to provide .env with OPENAI_API_KEY value to run it.
Steps Taken
This is really interesting as it’s going through various steps you can see on screen how it’s selecting various tags to achieve it.
Two more tools are worth noting for personal use cases scenarios. It works great for personal work automation. They are Nanobrowser and Nxtscape.
Long-Term Thinking
As this space is growing, I am expecting more tools to come in this space. So when you are planning to take it production, make sure you build some kind of abstraction on top of these tools. This will help you in switching to new tool in future. Remember the entire AI space is evolving very vast and you want to make sure your application is remaining stable in production env after QA cycle.
Remember, in the world of AI, adaptability is the real competitive advantage.
Rererence