How can test data generation and augmentation techniques be optimized to create challenging and varying scenarios that push the limits of an agent’s autonomous reasoning/adaptation abilities?
What do you think AI still can’t ‘get’ about real-world testing scenarios?
How can testing help balance autonomy with safety and compliance in intelligent agents?
How does AI affect community building?
What testing strategies can ensure reliability when agents generate code that interacts across multiple platforms and environments?
How does multi-modal testing (like what Replit is exploring) help validate code beyond text, including UI, speech, or visual outputs?
How can traditional unit and integration tests be adapted to validate code produced by autonomous agents?
What’s more important today test automation speed or test relevance?
What are some common shortcomings these tools have that a beginner should keep in mind when testing the output?
What unique challenges arise when testing systems where agents can make decisions independently, rather than following predefined rules
What approaches help maintain accountability and traceability for agent decisions?
How do you verify the tests are valid, providing value?
How to tackle safety while using agents for testing, are there any suggested standards or protocols to follow ?
Then we can consider that AI will be boost for the role of QA engineers/testers?
If agents are writing and executing code autonomously, who is ultimately accountable when things go wrong… the developer, the tester, or the AI itself? How should testing evolve to reframe accountability in this new setup?