From Hackathon to Real Impact: Accelerating UI Automation with Playwright

Automation hackathons are exciting, but the real value comes when the learning moves beyond the event and starts changing how teams work. During the recent Automation Hackathon, I focused on learning Playwright and applying it to real product scenarios. What began as an exploration quickly evolved into reusable automation that now saves time, reduces manual effort, and improves deployment confidence.

This blog shares the journey, the lessons learned, and how Playwright can transform UI testing when applied with the right mindset.

Why We Needed a Change in UI Testing

UI validation has traditionally been one of the most time-consuming parts of deployment:

• Manual smoke testing after deployments
• Dependency on product experts to verify flows
• Flaky or brittle automation that teams don’t trust
• Slow feedback loops delaying releases

The hackathon provided the perfect opportunity to explore a modern automation framework and experiment without risk to production workflows.

The goal was simple:
 Can we make UI validation fast, reliable, and accessible to team members?

Why Playwright?

Playwright stood out immediately for several reasons:

1. Reliability by Design

Playwright automatically waits for elements to be ready before interacting with them. This eliminates the need for excessive sleep() calls and significantly reduces flaky tests.

2. Real Browser Automation

It supports Chromium, Firefox, and WebKit, making tests closer to real user behavior.

3. Built-in Debugging Tools

Features like trace viewer, screenshots, and videos make failures easy to diagnose.

4. Perfect for End-to-End Scenarios

Playwright is designed for real user journeys, not just component testing.

These capabilities made it ideal for automating complex UI flows that previously required manual validation.

Hackathon Implementation: First Real Scenario

During the hackathon, I implemented an end-to-end automation scenario for an Internal Assistant Preview workflow.

This included:

• Logging into the platform
• Navigating across services
• Creating and launching an assistant
• Interacting with the UI and validating behavior

This was not a small proof-of-concept — it was a full user journey.

What I Learned Quickly

Real-world UI automation is not just about writing scripts. It requires:

• Handling dynamic UI behavior
• Managing popups and new tabs
• Working with asynchronous loading
• Creating robust selectors
• Building retry and wait strategies

This experience was the foundation for everything that followed.

The Turning Point: From Learning to Reuse

The biggest shift came after the hackathon.

Instead of stopping at a single scenario, I asked:

How can this help the entire team?

That question changed the direction of the work.

I extended the same Playwright patterns to another product and transformed the automation into a reusable deployment validation tool.

Scaling the Approach Across Teams

The Problem Teams Faced

During deployments, teams often needed to manually:

• Launch the UI
• Validate assistant creation
• Run sample interactions
• Confirm end-to-end functionality

This required:

• Time
• Context
• Product knowledge

And it had to be repeated for every deployment.

The Solution

I adapted and generalized the automation so that:

• Anyone can run the script
• No deep UI knowledge is required
• The same validation happens every time
• Deployments get fast feedback

This transformed automation from individual scripts into a shared team asset.

What Changed for the Team

This work didn’t just add tests — it changed workflows.

Before

• Manual validation required after deployment
• Knowledge siloed with UI experts
• Time spent repeating the same steps

After

• Reusable automation available to everyone
• Deployments validated quickly and consistently
• Significant reduction in manual effort
• Teams gained confidence in releases

This is the real value of automation:
 Changing how work happens, not just adding tests.

Technical Lessons from Building Robust Automation

Here are some key lessons that made the automation reliable.

1. Avoid Hard Waits

Instead of using fixed sleep times:

• Wait for elements to be visible
• Wait for buttons to be enabled
• Wait for network to be idle

This dramatically improves stability.

2. Use Roles and Accessible Selectors

Selectors based on roles and names are more stable than CSS paths.

Example:

page.get_by_role(“button”, name=”Create action”)

This makes tests resilient to UI layout changes.

3. Handle Popups and Tabs Properly

Modern apps often open new pages. Playwright’s popup handling ensures the script stays in control.

4. Build Reusable Helper Functions

Reusable click and wait utilities reduce duplication and increase consistency.

5. Think Like a User, Not a Script

Automate real user journeys — not individual clicks.

Overcoming Real-World Automation Challenges

One of the biggest realizations during this journey was how different real product automation is from tutorial examples. In practice, enterprise applications are complex, dynamic, and constantly evolving. Pages load content asynchronously, elements appear and disappear based on permissions, and workflows often span multiple tabs and services.

Instead of seeing these as blockers, this became an opportunity to build resilient automation patterns. Each failure helped uncover a new learning: how to wait for UI stability, how to handle duplicate elements, how to interact with modals, and how to manage timing issues caused by backend processing. Over time, the scripts evolved from simple step-by-step automation into robust, production-ready workflows.

Reducing Knowledge Silos

Another unexpected benefit was how automation helped reduce knowledge silos.

Previously, validating UI workflows required:

• Knowing where to click
• Understanding product flows
• Remembering hidden steps and workarounds

This knowledge often lived with a small group of experts. Automation turned that knowledge into executable documentation.

Now the workflow lives in code.
 Anyone can run it.
 Anyone can benefit from it.

This is a powerful shift toward shared ownership and transparency.

Final Reflection

Looking back, the most rewarding part of this journey was seeing how a short learning event could evolve into something with lasting impact. What started as an experiment became a reusable tool, a shared resource, and a step toward stronger automation practices.

This experience reinforced an important lesson:

The true value of learning is realized when it changes how we work and helps others succeed.

And this is only the beginning of what we can achieve with modern automation.