How AI Powered Playwright Agents Fit into the Traditional STLC (Software Testing Life Cycle)?

The Playwright team recently released an impressive new feature — i.e Playwright Agent

In this blog what I covered

1.How to Set Up the Playwright Agent.
2.Role of the Playwright Agent in the Traditional Software Testing Process.
3.Use Cases for Different Playwright Agents — Planner, Generator, and Healer.

Today’s software testing process typically follows this flow:

Requirements → Test Planning → Test Design → Test Implementation → Test Execution → Maintenance.

Now, let’s see where each AI-driven Playwright Agent aligns and enhances this traditional pipeline 👇

Playwright Agent looks something like mentioned in below diagram

One of the Amazing video from

Debbie O'Brien

On Playwright Agent

Before moving into detail about Playwright Agent, let’s FIRST see how we can set-up Playwright Agent

How to Set up Playwright Agent

Pre-request
Make sure you have Version: 1.105.0

Pre-request : Vs code version should be Version: 1.105.0 and Playwright latest
version 1.56

Step 1

Create a new directory and install Playwright latest version with below command

npm init playwright@latest      

Step 2

Start with adding Playwright Agent definitions to your project using the init-agents command. These definitions should be regenerated whenever Playwright is updated to pick up new tools and instructions.

npx playwright init-agents --loop=vscode

Above command will generate the agents. Once the agents have been generated, you can use your AI tool of choice to command these agents to build Playwright Tests.

Step 3

Now open NEW Github copilot Chat window and you will see THREE more items in the list i.e Planner,Generator and Healer attached below.

Now lets see the ROLE of each agents in actual and how it can fit with traditional testing Phase.

Playwright Agent

Now lets explain the Playwright agents one by one and see how we can map these agents with traditional Testing life cycle

🎭 Planner → Enhancing the Test Planning & Design Phase

Where it fits:
Between Requirements Analysis and Test Design.

==> Traditional:

• QA engineers manually explore the application, identify scenarios, and write test cases.
• This is time-consuming and often repetitive every sprint.

==> With AI Planner:

• The Planner automatically explores the app UI and identifies user journeys.
• It then creates a structured Markdown test plan, capturing:
• Test objectives
• Preconditions
• Steps & expected outcomes

==> Impact:

• Accelerates test design from hours to minutes.
• Ensures test coverage even for new or changed areas.
• Promotes collaboration — Markdown plans are readable by both QA and dev teams.

==> In short:

Planner = Your AI Test Analyst who drafts test cases intelligently.

🎭 Generator → Automating the Test Implementation Phase

Where it fits:
In the Test Implementation (Automation Development) phase.

==> Traditional:

• Automation engineers convert manual test cases into scripts (e.g., Playwright, Selenium).
• Requires effort, coding expertise, and maintenance discipline.

==> With AI Generator:

The Generator reads the Markdown test plan and produces ready-to-run Playwright test scripts.

Automatically handles:

• Page navigation
• Locators & selectors
• Assertions & waits
• Test structure & reusable components

==> Impact:

• Reduces manual scripting effort by 50–70%.
• Standardizes test code quality across teams.
• Frees up engineers to focus on complex edge cases and framework improvements.

In short:

Generator = Your AI Automation Engineer who instantly converts plans into working tests.

🎭 Healer → Powering the Test Execution & Maintenance Phase

==> Where it fits:
In the Test Execution, Monitoring, and Maintenance phases.

==> Traditional:

• Tests often fail due to UI changes, locator updates, or timing issues.
• Engineers spend significant time debugging and fixing flaky tests.

==> With AI Healer:

• The Healer monitors test executions, detects failures, and auto-repairs broken tests.
• It analyzes why a test failed (e.g., changed button ID, DOM shift) and updates selectors or logic.
• It can even suggest or apply fixes in real time.

==> Impact:

• Greatly reduces test maintenance overhead.
• Keeps CI/CD pipelines stable and reliable.
• Enables true self-healing test automation — a long-standing goal in QA.

In short:

Healer = Your AI Maintenance Engineer who keeps tests healthy and running continuously

Now lets see how Playwright Agent fit with existing STLC in detail below.

Playwright Agent Vs traditional STLC (software testing life cycle)

The diagram maps different stages of the Software Testing Life Cycle (STLC) to specialized Playwright Agents that handle specific automation roles.

Each agent corresponds to a testing phase and helps streamline the test process through AI-driven assistance.

Playwright Agents

The table explains how different stages of the Software Testing Life Cycle (STLC) connect with specific Playwright AI Agents that automate parts of the testing process.

Use Cases

Here are some use cases that explain how we can use Playwright Agent In actual. Give prompt in GitHub copilot with providing appropriate context.

Planner Agent

Prompt :

Generate test plan for login screen for site https://shop.qaautomationlabs.com/shop.php

In the below screenshot you can see testplan .md is created. Now we use this test plan file to execute the tests cases.

Test plan is created see the below attached screenshot.

Next step is to execute the above test plan.

Generate Agent

Prompt :

Generate test case for ### 2. Negative — Wrong password

Make sure pass login_test_plan.md file in the context when we run the above Prompt.

Lets generate test script for point ##2 from test plan.

In the below screenshot you can see .spec file is created with name negative-wrong-password.spec.ts.

Since in agent test cases also executed so you can see in the below screenshot .spec file is created and executed.

Healer Agent

Now lets see how healer agent works.

NOTE :

I intentionally fail the above test cases so that i can show how healer Agent work with updating name of the fields.

Prompt 1

Run the test case

In the below screenshot you can see test cases fail because name was incorrect.

In the below screenshot you can see healer agent suggested the reason of failure.

Now lets see how Playwright healer fix this issue

In the below screenshot you can see healer fix the Playwright test case and passed successfully.

Conclusion

In conclusion, the Playwright Agent represents a significant step forward in modernizing software testing. By combining automation, intelligence, and adaptability through agents like the Planner, Generator, and Healer, Playwright empowers teams to deliver more reliable, faster, and maintainable test suites. Integrating these agents into traditional testing workflows not only reduces manual effort but also enhances overall test efficiency and quality.