High quality assurance (QA) testing has lengthy been the spine of software program growth, however conventional QA approaches haven’t stored tempo with trendy growth cycles and complicated UIs. Most organizations nonetheless depend on a hybrid strategy combining handbook testing with script-based automation frameworks like Selenium, Cypress, and Playwright—but groups spend important quantity of their time sustaining present check automation slightly than creating new assessments. The issue is that conventional automation is brittle. Take a look at scripts break with UI adjustments, require specialised programming data, and sometimes present incomplete protection throughout browsers and units. With many organizations actively exploring AI-driven testing workflows, present approaches are inadequate.
On this submit, we discover how agentic QA automation addresses these challenges and stroll by way of a sensible instance utilizing Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a pattern retail utility.
Advantages of agentic QA testing
Agentic AI shifts QA testing from rule-based automation to clever, autonomous testing techniques. In contrast to typical automation that follows preprogrammed scripts, agentic AI can observe, be taught, adapt, and make selections in actual time. The important thing benefits embody autonomous check technology by way of UI remark and dynamic adaptation as UI parts change—minimizing the upkeep overhead that consumes QA groups’ time. These techniques mimic human interplay patterns, ensuring testing happens from a real person perspective slightly than by way of inflexible, scripted pathways.
AgentCore Browser for large-scale agentic QA testing
To comprehend the potential of agentic AI testing at enterprise scale, organizations want strong infrastructure that may help clever, autonomous testing brokers. AgentCore Browser, a built-in software of Amazon Bedrock AgentCore, addresses this want by offering a safe, cloud-based browser atmosphere particularly designed for AI brokers to work together with web sites and functions.
AgentCore Browser contains important enterprise safety features similar to session isolation, built-in observability by way of dwell viewing, AWS CloudTrail logging, and session replay capabilities. Working inside a containerized ephemeral atmosphere, every browser occasion could be shut down after use, offering clear testing states and optimum useful resource administration. For giant-scale QA operations, AgentCore Browser can run a number of browser classes concurrently, so organizations can parallelize testing throughout completely different situations, environments, and person journeys concurrently.
Agentic QA with the Amazon Nova Act SDK
The infrastructure capabilities of AgentCore Browser develop into actually highly effective when mixed with an agentic SDK like Amazon Nova Act. Amazon Nova Act is an AWS service that helps builders construct, deploy, and handle fleets of dependable AI brokers for automating manufacturing UI workflows. With this SDK, builders can break down advanced testing workflows into smaller, dependable instructions whereas sustaining the power to name APIs and carry out direct browser manipulation as wanted. This strategy provides seamless integration of Python code all through the testing course of. Builders can interleave assessments, breakpoints, and assertions instantly throughout the agentic workflow, offering unprecedented management and debugging capabilities. This mixture of the AgentCore Browser cloud infrastructure with the Amazon Nova Act agentic SDK creates a complete testing ecosystem that transforms how organizations strategy high quality assurance.
Sensible implementation: Retail utility testing
For example this transformation in follow, let’s contemplate growing a brand new utility for a retail firm. We’ve created a mock retail net utility to reveal the agentic QA course of, assuming the appliance is hosted on AWS infrastructure inside a non-public enterprise community throughout growth and testing phases.
To streamline the check creation course of, we use Kiro, an AI-powered coding assistant to mechanically generate UI check circumstances by analyzing our utility code base. Kiro examines the appliance construction, opinions present check patterns, and creates complete check circumstances following the JSON schema format required by Amazon Nova Act. By understanding the appliance’s options—together with navigation, search, filtering, and type submissions—Kiro generates detailed check steps with actions and anticipated outcomes which might be instantly executable by way of AgentCore Browser. This AI-assisted strategy dramatically accelerates check creation whereas offering complete protection. The next demonstration exhibits Kiro producing 15 ready-to-use check circumstances for our QA testing demo utility.
After the check circumstances are generated, they’re positioned within the check information listing the place pytest mechanically discovers and executes them. Every JSON check file turns into an unbiased check that pytest can run in parallel. The framework makes use of pytest-xdist to distribute assessments throughout a number of employee processes, mechanically using out there system assets for optimum efficiency.
Throughout execution, every check will get its personal remoted AgentCore Browser session by way of the Amazon Nova Act SDK. The Amazon Nova Act agent reads the check steps from the JSON file and executes them—performing actions like clicking buttons or filling varieties, then validating that anticipated outcomes happen. This data-driven strategy means groups can create complete check suites by merely writing JSON information, while not having to write down Python code for every check situation. The parallel execution structure considerably reduces testing time. Exams that may usually run sequentially can now execute concurrently throughout a number of browser classes, with pytest managing the distribution and aggregation of outcomes. An HTML report is mechanically generated utilizing pytest-html and the pytest-html-nova-act plugin, offering check outcomes, screenshots, and execution logs for full visibility into the testing course of.
One of the highly effective capabilities of AgentCore Browser is its skill to run a number of browser classes concurrently, enabling true parallel check execution at scale. When pytest distributes assessments throughout employee processes, every check spawns its personal remoted browser session within the cloud. This implies your total check suite can execute concurrently slightly than ready for every check to finish sequentially.
The AWS Administration Console gives full visibility into these parallel classes. As demonstrated within the following video, you may view the lively browser classes working concurrently, monitor their standing, and observe useful resource utilization in actual time. This observability is crucial for understanding check execution patterns and optimizing your testing infrastructure.
Past simply monitoring session standing, AgentCore Browser provides dwell view and session replay options to look at precisely what Amazon Nova Act is doing throughout and after check execution. For an lively browser session, you may open the dwell view and observe the agent interacting along with your utility in actual time—clicking buttons, filling varieties, navigating pages, and validating outcomes. Once you allow session replay, you may view the recorded occasions by replaying the recorded session. This lets you validate check outcomes even after the check execution completes. These capabilities are invaluable for debugging check failures, understanding agent habits, and gaining confidence in your automated testing course of.
For full deployment directions and entry to the pattern retail utility code, AWS CloudFormation templates, and pytest testing framework, confer with the accompanying GitHub repository. The repository contains the required elements to deploy and check the appliance in your personal AWS atmosphere.
Conclusion
On this submit, we walked by way of how AgentCore Browser can assist parallelize agentic QA testing for net functions. An agent like Amazon Nova Act can carry out automated agentic QA testing with excessive reliability.
In regards to the authors
Kosti Vasilakakis is a Principal PM at AWS on the Agentic AI group, the place he has led the design and growth of a number of Bedrock AgentCore providers from the bottom up, together with Runtime, Browser, Code Interpreter, and Id. He beforehand labored on Amazon SageMaker since its early days, launching AI/ML capabilities now utilized by 1000’s of corporations worldwide. Earlier in his profession, Kosti was an information scientist. Outdoors of labor, he builds private productiveness automations, performs tennis, and enjoys life along with his spouse and children.
Veda Raman is a Sr Options Architect for Generative AI for Amazon Nova and Agentic AI at AWS. She helps prospects design and construct Agentic AI options utilizing Amazon Nova fashions and Bedrock AgentCore. She beforehand labored with prospects constructing ML options utilizing Amazon SageMaker and likewise as a serverless options architect at AWS.
Omkar Nyalpelly is a Cloud Infrastructure Architect at AWS Skilled Providers with deep experience in AWS Touchdown Zones and DevOps methodologies. His present focus facilities on the intersection of cloud infrastructure and AI applied sciences—particularly leveraging Generative AI and agentic AI techniques to construct autonomous, self-managing cloud environments. By means of his work with enterprise prospects, Omkar explores revolutionary approaches to scale back operational overhead whereas enhancing system reliability. Outdoors of his technical pursuits, he enjoys taking part in cricket, baseball, and exploring artistic images. He holds an MS in Networking and Telecommunications from Southern Methodist College.
Ryan Canty is a Options Architect at Amazon AGI Labs with over 10 years of software program engineering expertise, specializing in designing and scaling enterprise software program techniques throughout a number of expertise stacks. He works with prospects to leverage Amazon Nova Act, an AWS service for constructing and deploying extremely dependable AI brokers that automate UI-based workflows at scale, bridging the hole between cutting-edge AI capabilities and sensible enterprise functions.
