Join Mayank Bhola (Co-Founder & Head of Product at LambdaTest), Shantanu Wali (Director of Product Management), and Prince Verma (Engineering Manager) for Intent Over Scripts: Modernizing Software Testing with AI.
This session shows how teams can move from brittle, script-heavy testing toward an intent-driven, AI-native model. See how AI handles scaffolding while humans stay focused on intent and outcomes, leading to faster, more reliable test coverage.
Key insights include authoring by intent, securing configs across environments, running parallel tests for quicker feedback, and treating tests like versioned code.
Book your free spot today and learn how AI is reshaping QA practices!
Here are some of the Q&As from this session:
Can KaneAI connect with Figma?
Is KaneAI is paid agent AI or is it free source
how can QA teams ensure that test outcomes remain explainable and trustworthy, especially when AI auto-heals locators or adapts test flows on the fly?
Can it take a feature requirement or a design document and generate test cases based on that ?
Is KaneAI needs to entire Repo access along with Git?
The code that i just saw looks very brittle and unmaintainable. Can you describe your process on how the code is to be kept maintainable. Can the tool support popular patterns like POM BDD etc
This looks good for a demo, but how can we accommodate KaneAI in real projects where certain client-level restrictions are in place?
Given the inherent “black box” nature of many AI models, what new techniques are necessary to achieve sufficient transparency and explain ability in intent-based testing?
How can AI-driven intent-based testing methodologies be fully integrated into existing CI/CD pipelines and DevOps workflows (to provide continuous feedback loops and adapting test strategies in real-time as software evolves and deploys)?
What’s the best first step to take to modernize from legacy software toward software testing with AI?
What’s the best way to structure a PRD prompt for an AI to get the most useful output?
What are the common accessibility gaps you’ve seen teams overlook during regression testing, and how can we address them systematically?
Can KaneAI be used to test mobile apps?
How to pass data from external file to the test cases?
Can AI detect regressions hidden by micro-copy or A/B changes?
Does KaneAI support testing of native apps on Android and iOS?
Can existing TCs be imported and refactored.
How does KaneAI ensure that its learning is restricted to a project/customer and not become public?
is it purely black-box? is it aware of API calls / does it test them independent of UI?
