What best practices help fine-tune AI tools to align with real testing needs?
How do you define success metrics for AI micro-deployments when business outcomes take months to show impact?
How do you create escalation paths for AI failures without overloading SMEs with low-value interventions?
How do you differentiate between harmless AI quirks and critical failures that impact business outcomes?
What architectural guardrails can stop AI from hallucinating or overfitting in test automation contexts?
How do you distinguish between AI being “innovative” vs. just being “weird”?
Could AI learn to recognize its own weird behavior and auto-correct?
Do you think “AI weirdness” will disappear with maturity, or is it something we’ll always need to manage?
How can we tackle AI Hallucinations?
How do you create escalation paths for AI failures without overloading SMEs with low-value interventions?
Can you run multiple customize agents during a test and all be accurate?