Revolutionizing Quality Assurance: The Future of Agentic AI Testing

Introduction to Modern QA Challenges

Benefits of Agentic QA Testing

AgentCore Browser for Large-Scale Agentic QA Testing

Implementing Agentic QA with the Amazon Nova Act SDK

Practical Implementation: Testing a Retail Application

Conclusion: Embracing the Future of QA Automation

About the Authors

Revolutionizing Quality Assurance: The Rise of Agentic QA Testing

Quality assurance (QA) testing has long been the backbone of software development, ensuring that applications function as intended and delivering a smooth user experience. However, traditional QA approaches have struggled to keep pace with modern development cycles and complex user interfaces (UIs). Most organizations still depend on a hybrid approach that combines manual testing with script-based automation frameworks like Selenium, Cypress, and Playwright. Unfortunately, this often results in teams investing significant time in maintaining existing test automation rather than focusing on creating new tests. The crux of the issue lies in the brittleness of traditional automation, where test scripts break with UI changes, necessitate specialized programming knowledge, and frequently provide incomplete coverage across diverse browsers and devices.

As organizations explore AI-driven testing workflows, it’s becoming increasingly clear that current methods are insufficient. In this post, we’ll delve into how agentic QA automation addresses these challenges through an innovative framework, with a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail application.

Benefits of Agentic QA Testing

Agentic AI represents a fundamental shift from rule-based automation to intelligent, autonomous testing systems. Unlike traditional automation, which operates on pre-programmed scripts, agentic AI can observe, learn, adapt, and make decisions in real time. This paradigm shift offers several significant advantages:

• Autonomous Test Generation: By observing UI components, agentic systems can generate tests dynamically, significantly reducing the maintenance workload for QA teams.
• Dynamic Adaptation: These systems can adjust as UI elements change, allowing them to maintain test coverage without constant intervention.
• Human-like Interaction Patterns: Unlike rigid scripts, agentic AI mimics actual human user behavior, ensuring that testing occurs from a genuine user perspective.

AgentCore Browser for Large-Scale Agentic QA Testing

To fully harness the potential of agentic AI at an enterprise scale, robust infrastructure is essential. The AgentCore Browser, a built-in tool of Amazon Bedrock AgentCore, provides a secure, cloud-based environment tailored for AI agents to interact with websites and applications.

Key features of the AgentCore Browser include:

• Enterprise Security: With session isolation, observability through live viewing, AWS CloudTrail logging, and session replay capabilities, QA teams can maintain a secure testing environment.
• Containerized Environment: Each browser instance operates in an ephemeral container, ensuring a clean testing state and optimal resource management.
• Concurrent Sessions: Organizations can run multiple browser sessions simultaneously, enabling parallel testing across various scenarios, environments, and user journeys.

Agentic QA with the Amazon Nova Act SDK

The capabilities of the AgentCore Browser reach new heights when combined with the Amazon Nova Act SDK. This AWS service allows developers to build, deploy, and manage fleets of reliable AI agents for automating production UI workflows.

With Amazon Nova Act, developers can streamline complex testing workflows into smaller, dependable commands while maintaining the ability to perform direct browser manipulation and API calls as needed. This seamless integration of Python code throughout the testing process allows for interleaving tests, breakpoints, and assertions directly within the agentic workflow, offering unparalleled control and debugging capabilities.

Practical Implementation: Retail Application Testing

To exemplify this transformational approach, let’s consider the development of a new application for a retail company. We’ve created a mock retail web application hosted on AWS to demonstrate the agentic QA process.

To expedite the test creation, we leverage Kiro—an AI-powered coding assistant—to automatically generate UI test cases by analyzing the application code base. Kiro evaluates the application’s structure, reviews existing test patterns, and produces comprehensive test cases in the JSON schema format required by Amazon Nova Act. By understanding key features like navigation, search, filtering, and form submissions, Kiro generates actionable test steps that are immediately executable through AgentCore Browser.

Once generated, these JSON test cases are automatically discovered and executed by pytest. Each test file operates independently, allowing parallel execution through the pytest-xdist framework, which optimally distributes tests across multiple worker processes.

Each test is run in an isolated AgentCore Browser session via the Amazon Nova Act SDK, executing steps like clicking buttons or filling out forms while validating expected results. This data-driven approach simplifies test creation, allowing teams to focus on writing JSON files instead of Python code for each scenario. As a result, parallel execution reduces testing time significantly, empowering organizations to efficiently manage extensive test suites.

AWS Management Console enhances visibility as it enables real-time monitoring and resource utilization tracking across parallel browser sessions. Furthermore, features like live view and session replay allow teams to observe agent interactions in real time or review recorded events for insightful debugging.

Conclusion

In this post, we explored the transformative impact of AgentCore Browser and Amazon Nova Act on agentic QA testing. This modern approach allows organizations to automate testing with high reliability and efficiency, simplifying the process while ensuring comprehensive coverage across UIs and devices.

About the Authors

Kosti Vasilakakis is a Principal PM at AWS on the Agentic AI team, having led the design of Bedrock AgentCore services. Previously with Amazon SageMaker, Kosti combines his data science background with a passion for creating productivity automations.

Veda Raman is a Sr Solutions Architect at AWS, specializing in designing Agentic AI solutions using Amazon Nova models. Her expertise spans building ML solutions and serverless architectures.

Omkar Nyalpelly is a Cloud Infrastructure Architect at AWS focused on the intersection of cloud infrastructure and AI technologies. Through his work, he seeks to reduce operational overhead while enhancing reliability.

Ryan Canty is a Solutions Architect at Amazon AGI Labs, helping customers bridge cutting-edge AI capabilities and real-world applications through Amazon Nova Act. With over a decade of experience, he specializes in scalable enterprise software design.

For complete deployment instructions and to access the sample retail application code, AWS CloudFormation templates, and pytest testing framework, refer to the accompanying GitHub repository. Start your journey toward revolutionizing QA today!