High quality assurance (QA) testing has lengthy been the spine of software program improvement, however conventional QA approaches haven’t stored tempo with fashionable improvement cycles and complicated UIs. Most organizations nonetheless depend on a hybrid method combining guide testing with script-based automation frameworks like Selenium, Cypress, and Playwright—but groups spend important quantity of their time sustaining current check automation relatively 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 infrequently present incomplete protection throughout browsers and gadgets. With many organizations actively exploring AI-driven testing workflows, present approaches are inadequate.
On this publish, we discover how agentic QA automation addresses these challenges and stroll by means 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 methods. Not like standard automation that follows preprogrammed scripts, agentic AI can observe, be taught, adapt, and make choices in actual time. The important thing benefits embrace autonomous check era by means of UI statement and dynamic adaptation as UI parts change—minimizing the upkeep overhead that consumes QA groups’ time. These methods mimic human interplay patterns, ensuring testing happens from a real consumer perspective relatively than by means of inflexible, scripted pathways.
AgentCore Browser for large-scale agentic QA testing
To appreciate 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 device of Amazon Bedrock AgentCore, addresses this want by offering a safe, cloud-based browser setting particularly designed for AI brokers to work together with web sites and purposes.
AgentCore Browser contains important enterprise safety features resembling session isolation, built-in observability by means of stay viewing, AWS CloudTrail logging, and session replay capabilities. Working inside a containerized ephemeral setting, every browser occasion could be shut down after use, offering clear testing states and optimum useful resource administration. For big-scale QA operations, AgentCore Browser can run a number of browser periods concurrently, so organizations can parallelize testing throughout totally different eventualities, environments, and consumer journeys concurrently.
Agentic QA with the Amazon Nova Act SDK
The infrastructure capabilities of AgentCore Browser turn out to be 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 flexibility to name APIs and carry out direct browser manipulation as wanted. This method gives seamless integration of Python code all through the testing course of. Builders can interleave assessments, breakpoints, and assertions immediately inside the agentic workflow, offering unprecedented management and debugging capabilities. This mix of the AgentCore Browser cloud infrastructure with the Amazon Nova Act agentic SDK creates a complete testing ecosystem that transforms how organizations method high quality assurance.
Sensible implementation: Retail utility testing
As an example this transformation in follow, let’s take into account creating a brand new utility for a retail firm. We’ve created a mock retail net utility to exhibit the agentic QA course of, assuming the appliance is hosted on AWS infrastructure inside a personal enterprise community throughout improvement and testing phases.
To streamline the check creation course of, we use Kiro, an AI-powered coding assistant to robotically generate UI check instances by analyzing our utility code base. Kiro examines the appliance construction, evaluations current check patterns, and creates complete check instances 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 are instantly executable by means of AgentCore Browser. This AI-assisted method dramatically accelerates check creation whereas offering complete protection. The next demonstration reveals Kiro producing 15 ready-to-use check instances for our QA testing demo utility.
After the check instances are generated, they’re positioned within the check knowledge listing the place pytest robotically 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, robotically using out there system assets for optimum efficiency.
Throughout execution, every check will get its personal remoted AgentCore Browser session by means 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 kinds, then validating that anticipated outcomes happen. This data-driven method means groups can create complete check suites by merely writing JSON recordsdata, with no need to put in writing Python code for every check situation. The parallel execution structure considerably reduces testing time. Exams that will usually run sequentially can now execute concurrently throughout a number of browser periods, with pytest managing the distribution and aggregation of outcomes. An HTML report is robotically 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 vital highly effective capabilities of AgentCore Browser is its means to run a number of browser periods 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 relatively than ready for every check to finish sequentially.
The AWS Administration Console gives full visibility into these parallel periods. As demonstrated within the following video, you’ll be able to view the energetic browser periods operating concurrently, monitor their standing, and monitor useful resource utilization in actual time. This observability is vital for understanding check execution patterns and optimizing your testing infrastructure.
Past simply monitoring session standing, AgentCore Browser gives stay view and session replay options to look at precisely what Amazon Nova Act is doing throughout and after check execution. For an energetic browser session, you’ll be able to open the stay view and observe the agent interacting together with your utility in actual time—clicking buttons, filling kinds, navigating pages, and validating outcomes. Once you allow session replay, you’ll be able to 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, check with the accompanying GitHub repository. The repository contains the required elements to deploy and check the appliance in your individual AWS setting.
Conclusion
On this publish, we walked by means of how AgentCore Browser can assist parallelize agentic QA testing for net purposes. An agent like Amazon Nova Act can carry out automated agentic QA testing with excessive reliability.
Concerning the authors
Kosti Vasilakakis is a Principal PM at AWS on the Agentic AI workforce, the place he has led the design and improvement of a number of Bedrock AgentCore companies from the bottom up, together with Runtime, Browser, Code Interpreter, and Identification. 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 in addition as a serverless options architect at AWS.
Omkar Nyalpelly is a Cloud Infrastructure Architect at AWS Skilled Companies 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 methods to construct autonomous, self-managing cloud environments. Via his work with enterprise prospects, Omkar explores progressive approaches to cut back operational overhead whereas enhancing system reliability. Outdoors of his technical pursuits, he enjoys enjoying cricket, baseball, and exploring inventive pictures. 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 methods 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 purposes.
