# AI Agents at Every Step of the Testing Life Cycle From test creation, maintenance to failure analysis, our AI agents are embedded across every phase of testing-transforming Test Management to accelerate your entire workflow. ## Test case generator agent Convert any requirement artifact into actionable test cases instantly. Create test cases 90% faster and improve test coverage by 50%. #### Auto-generate test cases in seconds Let AI generate context-aware test cases by analyzing Product Requirement Documents, Jira stories, images, Confluence, and multiple file types-with full support for multi-file inputs. #### **Auto-generate test case details** Auto-populate different test case fields such as Description, Test Steps, Expected Results, Preconditions, Priority, etc, for creating comprehensive & effective test cases. ## Test deduplication agent Automatically identify test cases that are exact or semantically similar across your repository. Get intelligent recommendations for merging or removing duplicate test cases. [Test Deduplication AI Agent \| Test Management](https://www.youtube.com/watch?v=tobFi6d5a8Q) ## Low-code authoring agent Accelerate test development by converting test cases into low-code automated tests. [Low-Code Authoring Agent \| Test Management](https://www.youtube.com/watch?v=lJjdBjUJp2E) ## Test Failure Analysis Agent Synthesises test reports, logs, stack traces, history, similar failures and more to identify failure root cause, type, and suggest actionable next steps for remediation. [BrowserStack Test Failure Analysis agent](https://www.youtube.com/watch?v=9bdW0ifGEW8) ##### Root cause analysis Synthesises multiple inputs(test logs, history, smart tags & more) to give an explanation for the failure ##### Failure categorisation Categorizes failure as product bug, environment issue, automation issue or any custom issue based on the RCA ##### Failure Remediation Get next steps to remediate the failure or report the bug along with necessary artifacts and fixes ## Test selection agent Take the guesswork out of deciding which tests to execute. Identify the most relevant tests in a test run. [Test Selection Agent \| BrowserStack Test Management](https://www.youtube.com/watch?v=0jCo6aTPO-c) ## Test Data Generator Agent Create context-aware test data instantly and reduce dependency on manual data setup. [Test Data Generator Agent \| BrowserStack Test Management](https://www.youtube.com/watch?v=IHgXpTks3ls) ## Test Maintenance Agent Keep test cases up to date with automatic updates, reusable step suggestions, smart tagging, and traceability along with change alerts and impact analysis. [Test Case Maintenance Agent \| Test Management](https://www.youtube.com/watch?v=NdS1Lg0Bkfw) ## Test Planning Agent Transform requirement documents, technical specifications, and past test data into comprehensive test plans with the scope, schedule, risk analysis, and execution milestones. ## Frequently Asked Questions #### What are the AI use cases currently present in Test Management? Test Management provides AI capabilities across the entire testing lifecycle—from test creation and planning to execution, analysis, and maintenance. AI helps generate test cases from requirements, intelligently select relevant tests to run, identify and remove duplicates, analyze test failures to pinpoint root causes, and keep your test suite up-to-date as your application evolves. #### What are the benefits of enabling BrowserStack AI in Test Management? BrowserStack AI significantly reduces time and effort in test case authoring and execution — enabling up to 90% faster test case creation and 10X faster automated test development. It also helps you improve test coverage by 50%. #### Can the AI generate tests based on custom documents or guidelines? Yes, users can provide detailed descriptions, select folders with related test cases, or upload requirement documents to generate context-based test cases. #### What data is used to generate test cases? Test cases are generated using existing test case details and additional context provided by users through requirement documents or custom prompts. #### How does BrowserStack ensure data accuracy? AI models generate test cases based on user input, such as descriptions or requirement documents. The accuracy of the output depends on the quality and completeness of the input provided. Currently, it provides 91% accuracy. #### Can test case generation support multiple languages? We have the capability to support multiple languages. #### What if the uploaded requirement doc/PRD is not complete/lacks some of the required information? BrowserStack AI will analyze the uploaded requirement doc and provide the recommendations based on the available context. You can also provide the missing data by uploading other requirement documents or by providing context through the user prompts. #### How is customer data protected within BrowserStack AI/ML systems? Customer data is strictly isolated, ensuring that no data from one customer crosses over to another. Strict checks are in place to ensure that only the relevant AI processes can access customer content, maintaining security and confidentiality. #### Will my data be used to train BrowserStack AI models? Currently, we do not train or fine-tune AI models on customer data. Our approach leverages foundation models, which are pre-trained and not fine-tuned with any customer data. #### How is data separation maintained between clients during AI model processing? Customer data is processed in real-time using online prediction (one-shot inference) without storage. Data remains isolated throughout the inference process, following stringent security protocols aligned with industry standards. #### Do BrowserStack AI features use the same AI models across features or different ones? We orchestrate different third-party AI tools depending on the use case to optimize results, performance, and cost. This configuration is pre-defined and not customizable. For the list of third-party tools, refer to: [https://www.browserstack.com/terms/ai-terms](/content/terms/ai-terms/index.html).