With MCP, Playwright Took the AI Flight: Can It Soar Higher with OpenAI’s CUA?

The test automation landscape is experiencing a seismic shift, not because of yet another wave of codeless tools or smarter element selectors, but because of the growing infusion of machine reasoning and contextual adaptability into the heart of the testing lifecycle.

At the center of this evolution are two groundbreaking advancements:

• Model Context Protocol (MCP) - an interoperability standard introduced by Anthropic that enables large language models (LLMs) to seamlessly interact with tools, APIs, and contextual data sources.

• Computer-Using Agent (CUA) - an emerging capability from OpenAI that allows AI agents to interact with digital interfaces visually and functionally, mimicking how humans use a computer.

  
  

For those of us in the software testing world, Playwright is already a powerful and well-respected automation framework renowned for its speed, cross-browser support, and developer-centric flexibility. But when Playwright is paired with MCP and now CUA, it moves beyond scripting and into the realm of semantic automation, intent-driven test orchestration, and even self-adjusting execution.

The dream of autonomous testing has long seemed just out of reach but these innovations are rapidly bringing it into the present.

From Script Runner to Smart Agent: Playwright + MCP

With the introduction of MCP, Playwright is no longer just a test execution engine but becomes a semantic interface that AI agents can operate. MCP enables large models to:

• Remote execution,

• Invoke functions via structured protocols,

• And reason over outcomes to adapt next steps.

When used together, MCP and Playwright form a powerful duo: the agent interprets what needs to be tested, and Playwright executes that intent with high precision. The brittle, step-by-step scripting of old begins to give way to intent-driven testing where high-level goals are enough.

Computer-Using Agent

Now comes the most exciting part. OpenAI’s newly released Automated Testing Agent built on their Computer-Using Agent (CUA) framework. This isn't just a test runner. It's an agent that can:

• Launch a browser,

• Visually interpret web interfaces,

• Generate test cases based on your prompts,

• And actually perform the interactions inside the browser  just like a human tester would.

And yes, it’s available to everyone on GitHub.

You provide the agent with a set of instructions describing the test scenario. The tool then automatically creates and executes those tests, without the need for step-by-step scripting. It even offers a configuration window where you can:

• Specify the URL of your system under test,

• Provide login parameters, if needed,

• And view results in an interactive browser context.

As a bonus, OpenAI includes a sample e-commerce application in this library, so you can quickly experiment with the tool before applying it to your own systems.

Does It Work? My Initial Take

I tried it.

For simple scenarios like login workflows, product navigation, cart validation, it was remarkably effective. The agent launched the browser, executed the steps, and responded appropriately to UI changes. For a first release, this is nothing short of impressive. I tried it on my company website Applause.com but the success was limited when compared to the demo app.

Is it ready to replace your entire test suite or a complex commercial testing platform? Not yet.

But that’s not the point yet :)

This is a glimpse into the future. A future where intelligent agents can take your high-level intent, understand the application visually, and interact with it intelligently without needing DOM hooks or API access. It’s the closest we’ve seen to a “true AI tester” in the wild.

Where Does This Leave Traditional Tools?

Will this spell the end of commercial testing tools? Probably not in the short term.

Enterprise-grade features like CI/CD integration, robust test data management, reporting dashboards, and compliance support still matter and they’re areas where mature platforms excel. But the core test creation and execution experience is ripe for disruption especially if the experience becomes as simple as describing a scenario in plain English.

What OpenAI is offering is not a finished product but it’s a provocation. A signal to the industry that test automation doesn’t have to be stuck in code or keyword models. It can be vision-driven, context-aware, and even simply conversational.

Final Thoughts: Can It Soar?

With MCP, Playwright took its first steps toward intelligent, AI-controlled automation. With CUA, OpenAI is pushing the envelope further towards truly autonomous testing powered by context and reasoning.

While it may not replace enterprise frameworks just yet, it raises an important question:

If an AI agent can understand the screen, interpret goals, and test applications on its own, what's the new role of the human tester?

One thing’s certain: we’re no longer asking if AI can test but how far it can go.

And from what I’ve seen, we’re just getting off the runway.