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?
What really stood out to me from the session was how vital testers are in making AI agents more autonomous, but with safety and control still in place. It’s not about micromanaging every decision the agent makes, but rather creating those important safety measures or “guardrails” that allow the AI to make its own choices while still being trustworthy and ethical.
Think of testers as the safety net, ensuring that even when the AI is acting independently, it’s always staying on track and doing the right thing.
After attending Luis Héctor Chávez’s session at TestMu 2025, one key point that really stood out to me is how crucial adaptability will be for testers in the future. As AI takes over more coding tasks, it’s no longer just about writing tests to match code but about thinking ahead.
We’ll need to anticipate those unpredictable behaviors that AI might introduce. It’s like expecting the unexpected and designing tests that can adapt to those changes.
What’s even more important is the shift from simply scripting to thinking strategically. It’s about being flexible, designing tests that can evolve, and understanding that AI might produce outcomes we didn’t plan for.
So, the ability to reason through those non-deterministic results and keep up with AI’s learning process will be what sets successful testers apart in the future. In short, adaptability is going to be our superpower moving forward.
When migrating tests to BiDi-based frameworks, a common pitfall is sticking to old practices like synchronous waits and polling. In BiDi, you need to think in terms of events and real-time reactions rather than relying on those static, traditional methods.
If you don’t make that shift, your tests can become flaky, and you’ll miss key signals that could affect test outcomes. It’s all about adapting your approach to the dynamic nature of BiDi to ensure smooth and accurate testing.
One point that really stood out to me was how critical thinking is going to be the key skill that keeps testers valuable, even as AI takes over more of the coding workload. Sure, automation can handle the repetitive tasks, but it’s our ability to dig deeper like understanding why an AI agent might fail or designing tests for those tricky edge cases that will continue to make testers indispensable. It’s not just about automating, it’s about applying our judgment and intuition to solve complex problems.
That’s something AI can’t replace anytime soon! This idea was highlighted throughout the keynote, and it really got me thinking about how important it is for us as testers to keep honing that skill.
During Luis Héctor Chávez’s keynote session, I got some really valuable insights into how software testing frameworks can help boost agent autonomy without compromising reliability or accountability. One of the key takeaways was the importance of modular checks. These are like safety nets that allow agents to explore different decisions but in a controlled, step-by-step way. So, while the agents have the freedom to make decisions, we can still trace their actions and ensure they’re working as expected.
Another crucial point was having rollback options. This means that if an agent makes a decision that doesn’t quite work out, we can always roll back to a previous, reliable state. It’s like having a fail-safe to make sure things don’t go sideways when an agent takes a wrong turn.
Lastly, structured logs were highlighted as a must-have. With clear, organized logs, we can track what the agent is doing at every step. This ensures accountability, making it easier to understand why a particular decision was made and providing a record if we ever need to check back.
In a nutshell, it’s all about finding the right balance giving agents the freedom to make decisions, while ensuring we can track and control those decisions when necessary. It’s a practical, safe way to let autonomy thrive without losing control.