Prompt Driven AI based Automation Framework using Playwright MCP in Python

1. Executive Summary

2. Core Concept of the Project

The framework is architected around a streamlined three-phase pipeline that decouples test intent from low-level DOM selectors and browser execution protocols:

3. Example Prompt & Structured Execution Plan

ChatGPT does not directly manipulate or control the browser. Instead, it parses natural language into an approved, strongly-typed execution plan that the local engine can safely evaluate.

[
  { "action": "login", "user": "qa_user" },
  { "action": "open_connector_page" },
  { "action": "create_connector" },
  { "action": "fill_credentials" },
  { "action": "validate_connection" },
  { "action": "verify_connector_active" }
]

4. Execution Architecture & Technology Stack

The execution layer maintains predefined MCP actions implemented in Python. ChatGPT can exclusively select from these approved operations, preventing arbitrary code execution while keeping the framework maintainable, resilient, and enterprise-secure.

5. Suggested Project Structure & Dependency Setup

A modular layout separating prompts, AI connectors, MCP action registries, and test execution harnesses:

6. Predefined MCP Actions & Execution Dispatcher

Explore the modular Python components that power the prompt-to-browser execution pipeline:

from playwright.sync_api import Page, expect

def login(page: Page, username: str, password: str):
    page.get_by_role('textbox', name='Username').fill(username)
    page.get_by_role('textbox', name='Password').fill(password)
    page.get_by_role('button', name='Sign in').click()

def open_connector_page(page: Page):
    page.get_by_role('link', name='Connectors').click()

def create_connector(page: Page):
    page.get_by_role('button', name='Add Connector').click()

def fill_credentials(page: Page, account_id: str, access_key: str):
    page.get_by_role('textbox', name='Account ID').fill(account_id)
    page.get_by_role('textbox', name='Access Key').fill(access_key)

def validate_connection(page: Page):
    page.get_by_role('button', name='Validate').click()

def verify_connector_active(page: Page):
    expect(page.get_by_text('Connector is active')).to_be_visible()

8. Writing Tests with Prompts & Enterprise Value

The test case is no longer an imperative script bound to brittle XPath/CSS locators—it is a clear, reusable business intention:

10. AI-Assisted Failure Analysis & Triage

When a step fails, the framework immediately captures the error stack, screenshot path, trace summary, and DOM snapshot. These details are packaged and analyzed via ChatGPT to generate instant root-cause diagnostics and locator remediation:

def analyze_failure(error_message: str, dom_summary: str) -> str:
    response = client.responses.create(
        model='gpt-4.1-mini',
        input=f'''
        Error: {error_message}
        DOM summary: {dom_summary}

        Explain the likely cause and suggest a stable locator strategy.
        '''
    )

    return response.output_text

11. Enterprise Security Considerations

12. CI/CD Integration (Jenkins & GitHub Actions)

Automated parallel execution and test artifact archiving seamlessly fit into enterprise delivery pipelines:

pipeline {
  agent any
  stages {
    stage('Setup') {
      steps {
        sh 'python -m venv venv'
        sh '. venv/bin/activate && pip install -r requirements.txt'
        sh '. venv/bin/activate && playwright install --with-deps'
      }
    }
    stage('Run Prompt Tests') {
      steps {
        sh '. venv/bin/activate && pytest -n auto'
      }
    }
    stage('Archive') {
      steps {
        archiveArtifacts artifacts: 'reports/**, traces/**, screenshots/**'
      }
    }
  }
}

13. End-to-End Execution Flow

14. Observed Results & Engineering Impact

15. Future Enhancements & Conclusion