The case for smarter testing: Automation in the era of AI

In modern Agile development, the velocity of code changes and feature releases makes it increasingly difficult for manual test automation scripting to keep pace. Testing teams are constantly under pressure to expand coverage and maintain quality. However, manual test scripting efforts can rarely scale as fast as product demands. The process is time-consuming and resource-intensive, often slowing Agile teams and threatening release timelines. Traditional scripting methods with frameworks like Playwright and TypeScript make broad coverage difficult and introduce bottlenecks that hinder quality.

This is why test automation must evolve!

The old ways won’t suffice anymore.  Teams need solutions that can quickly translate high-level intent into reliable, standardized scripts—without increasing errors or maintenance costs.

The foundation for intelligent test automation is built on a well-structured automation framework of Playwright + TypeScript + Cucumber (BDD) stack, implementing the Page Object Model (POM) to ensure scalability, maintainability of test suite with maximum code re-usability. All of this lays the groundwork on which intelligent agents like GitHub Copilot can thrive.

With this foundation in place, Agentic AI, exemplified by GitHub Copilot, is redefining the landscape by serving as a highly capable, context-aware coding assistant within existing workflows. Agentic AI refers to AI tools that assist in a more proactive manner by acting as intelligent agents. They are context-aware, adaptive, and capable of automating complex tasks with minimal human intervention.

Adopting an AI-augmented testing approach by integrating GitHub Copilot Agent into this test automation framework is a game-changing solution. Let’s see how and why this is so.

A copilot acts as a smart in-IDE, context-aware coding assistant that helps in contextually interpreting high-level prompts and Gherkin scenarios to auto-generate structured step definitions, page object methods, and utility functions. This allows streamlining the entire automation cycle. Human reviewers then refine and polish the AI-generated code to align it with coding standards, accelerating test automation while maintaining quality and structure.

Adapting to test automation in an AI-driven era

With a strong test automation setup foundation laid out, the following 4-step journey can help in transforming the test automation process.

Figure 1: AI-augmented test development flowchart

1. AI agent configuration

Unlocking Copilot’s true potential begins with a well configured environment, where Copilot Agent is installed within IDEs such as VS Code/IntellEJ/Eclipse, with context granted to POM classes, step definitions, and shared test utilities. Secure enterprise settings ensure no risk of code or data leaks.

By analyzing open files and repository-wide patterns, Copilot ensures contextual awareness and accordingly suggests code that matches existing project architecture and naming conventions. This deep understanding enables seamless generation of new test scripts and modules.

2. AI-assisted script generation

The journey from Gherkin scenarios to executable code begins when a QA writes a new Gherkin scenario or even a brief comment describing intent, Copilot instantly proposes the corresponding boilerplate code. For example, the step “Given the user is logged in” led Copilot to generate a correct TypeScript function using the loginPage.login() method. This dramatically cuts down repetitive scripting effort.

While Copilot handled the bulk of boilerplate and structure, QA engineers review, adjust, and finalize the scripts—ensuring compliance with business rules and test coverage objectives.

Multi-step processes and cross-page flows (e.g., adding products as admin and verifying in catalog) are drafted in seconds, allowing engineers to focus on edge case validation and business-critical logic thereby handling complex flows.

3. POM and Cucumber pattern integration

Copilot recognizes and utilizes established POM structures and Cucumber templates, producing code that matches team standards for method naming, assertions, and Playwright best practices (e.g., built-in waits, error handling). This improves codebase maintainability and reduces flaky timing issues.

Common Given/When/Then definitions and POM methods are also generated with high accuracy, promoting consistency and reusability. For new UI elements, Copilot quickly builds method stubs and locator strategies and jump-starting robust page objects.

4. Workflow transformation and prompt engineering

Teams shift from a linear, manual test automation scripting process to an iterative loop: describe the test scenario, let Copilot draft code, review, and execute. This approach significantly reduces context-switching and manual efforts.

The best results come from well-crafted prompts. Engineers can work towards developing best practices for phrasing Copilot requests (e.g., “Write a Playwright test for scenario X using Cucumber steps”), further improving AI output quality.

Copilot’s AI-driven flow is integrated with Playwright MCP (Model Context Protocol) alongside our automation stack. MCP enables large‑language models like Copilot to interact with live browser contexts using structured accessibility snapshots instead of relying on visual screenshot-based cues. This means Copilot-generated tests could automatically discover and validate locators, execute scripts immediately in a real session, and generate full trace-based reports for each run, which accelerates feedback loops and enhances reliability.

Parallel client sessions further enable real-time browser interactions and faster parallel execution. In practice, MCP improves speed of test validation, increases locator stability through structured DOM context, and enhances traceability of AI-generated automation, making our Agentic workflow more robust, auditable, and maintainable.

AI-generated deliverables

• Cucumber step definitions: Checkout workflow step sets generated in seconds, leveraging POMs and requiring minimal manual assertion additions.
• Page object methods: Placeholder methods (e.g., addItem, filterByCategory) for new UI were scaffolded automatically, later refined by engineers.
• Utility functions: Data parsing or email verification helpers built instantly from concise function names and comments.

And we see some remarkable results too!

Test development cycles accelerate dramatically, allowing faster creation of robust, maintainable test suites. Apart from ensuring broader coverage and minimal errors, this shift has enabled engineers to focus on complex testing which delivers higher quality, faster releases, and a stronger ROI in productivity.

For a real-world project implementation, the results were transformative!

Figure 2: AI-driven advantages

Human in the loop: Because AI needs a co-pilot, not an autopilot

While integrating AI-based solutions like GitHub Copilot is a game-changer, it’s only as smart as the human guiding it. Keeping QA experts in the review loop ensures precision, preserves business logic, and upholds quality standards. With proper training and smart prompt-tuning, teams can unlock the full potential of Copilot by blending speed with strategic oversight.

Effective prompt design techniques improve Copilot’s accuracy and reduce review cycles. Investing in this skill accelerates AI adoption.

Besides, with GitHub Copilot’s enterprise setup, code integrity and data privacy remains intact. It will ensure no code leaks or external data exposure. This keeps your development secure, compliant, and trusted at every step.

Conclusion

Clearly, AI-powered test automation is truly transforming QA, enabling teams to achieve more with less manual effort. Copilot’s integration has also ensured faster releases, better coverage, and improved product quality, without compromising on any standards or security benchmarks. The team plans to scale Copilot-assisted automation further to more projects. They also intend to embed it in CI workflows, while monitoring key metrics to drive continuous improvement.

Start small and experiment with Copilot in your automation stack. Measure outcomes and scale rapidly. The future of testing is here. Why wait?!