High quality assurance (QA) testing has lengthy been the spine of software program growth, however conventional QA approaches haven’t stored tempo with fashionable growth cycles and sophisticated UIs. Most organizations nonetheless depend on a hybrid method combining handbook testing with script-based automation frameworks like Selenium, Cypress, and Playwright—but groups spend important quantity of their time sustaining current take a look at automation somewhat than creating new assessments. The issue is that conventional automation is brittle. Take a look at scripts break with UI modifications, require specialised programming information, 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 means of a sensible instance utilizing Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a pattern retail software.
Advantages of agentic QA testing
Agentic AI shifts QA testing from rule-based automation to clever, autonomous testing methods. In contrast to typical automation that follows preprogrammed scripts, agentic AI can observe, study, adapt, and make selections in actual time. The important thing benefits embody autonomous take a look at technology by means of UI commentary 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 person perspective somewhat 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 assist clever, autonomous testing brokers. AgentCore Browser, a built-in instrument of Amazon Bedrock AgentCore, addresses this want by offering a safe, cloud-based browser surroundings particularly designed for AI brokers to work together with web sites and purposes.
AgentCore Browser contains important enterprise safety features equivalent to session isolation, built-in observability by means of stay viewing, AWS CloudTrail logging, and session replay capabilities. Working inside a containerized ephemeral surroundings, 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 grow to be really 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 provides seamless integration of Python code all through the testing course of. Builders can interleave assessments, breakpoints, and assertions instantly inside 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 method high quality assurance.
Sensible implementation: Retail software testing
For example this transformation in follow, let’s think about growing a brand new software for a retail firm. We’ve created a mock retail net software to show the agentic QA course of, assuming the applying is hosted on AWS infrastructure inside a personal enterprise community throughout growth and testing phases.
To streamline the take a look at creation course of, we use Kiro, an AI-powered coding assistant to robotically generate UI take a look at circumstances by analyzing our software code base. Kiro examines the applying construction, critiques current take a look at patterns, and creates complete take a look at circumstances following the JSON schema format required by Amazon Nova Act. By understanding the applying’s options—together with navigation, search, filtering, and kind submissions—Kiro generates detailed take a look at steps with actions and anticipated outcomes which can be instantly executable by means of AgentCore Browser. This AI-assisted method dramatically accelerates take a look at creation whereas offering complete protection. The next demonstration reveals Kiro producing 15 ready-to-use take a look at circumstances for our QA testing demo software.
After the take a look at circumstances are generated, they’re positioned within the take a look at knowledge listing the place pytest robotically discovers and executes them. Every JSON take a look at file turns into an unbiased take a look at that pytest can run in parallel. The framework makes use of pytest-xdist to distribute assessments throughout a number of employee processes, robotically using accessible system assets for optimum efficiency.
Throughout execution, every take a look at will get its personal remoted AgentCore Browser session by means of the Amazon Nova Act SDK. The Amazon Nova Act agent reads the take a look at steps from the JSON file and executes them—performing actions like clicking buttons or filling types, then validating that anticipated outcomes happen. This data-driven method means groups can create complete take a look at suites by merely writing JSON recordsdata, while not having to jot down Python code for every take a look at state of affairs. The parallel execution structure considerably reduces testing time. Assessments that might 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 robotically generated utilizing pytest-html and the pytest-html-nova-act plugin, offering take a look at outcomes, screenshots, and execution logs for full visibility into the testing course of.
Some of the highly effective capabilities of AgentCore Browser is its means to run a number of browser classes concurrently, enabling true parallel take a look at execution at scale. When pytest distributes assessments throughout employee processes, every take a look at spawns its personal remoted browser session within the cloud. This implies your complete take a look at suite can execute concurrently somewhat than ready for every take a look at to finish sequentially.
The AWS Administration Console gives full visibility into these parallel classes. As demonstrated within the following video, you possibly can view the energetic browser classes working concurrently, monitor their standing, and monitor useful resource utilization in actual time. This observability is important for understanding take a look at execution patterns and optimizing your testing infrastructure.
Past simply monitoring session standing, AgentCore Browser provides stay view and session replay options to observe precisely what Amazon Nova Act is doing throughout and after take a look at execution. For an energetic browser session, you possibly can open the stay view and observe the agent interacting along with your software in actual time—clicking buttons, filling types, navigating pages, and validating outcomes. Once you allow session replay, you possibly can view the recorded occasions by replaying the recorded session. This lets you validate take a look at outcomes even after the take a look at execution completes. These capabilities are invaluable for debugging take a look at failures, understanding agent conduct, and gaining confidence in your automated testing course of.
For full deployment directions and entry to the pattern retail software code, AWS CloudFormation templates, and pytest testing framework, discuss with the accompanying GitHub repository. The repository contains the mandatory parts to deploy and take a look at the applying in your individual AWS surroundings.
Conclusion
On this submit, we walked by means of how AgentCore Browser will help parallelize agentic QA testing for net purposes. 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 workforce, the place he has led the design and growth of a number of Bedrock AgentCore companies 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 hundreds 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 youngsters.
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 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. By his work with enterprise prospects, Omkar explores progressive 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 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 know-how 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.
