Systems | Development | Analytics | API | Testing

AI promises speed, but testers are often the first to notice where it quietly breaks down. Beneath the impressive outputs lie hidden issues like hallucinations, false confidence, and blind spots that can easily go unnoticed yet cause real damage if left unchecked. This atomic talk explores the subtle ways AI can fail, why speed without reliability is risky, and how testers play a critical role in supervising and strengthening AI systems. It highlights practical strategies for working alongside AI to make its outputs more trustworthy, reliable, and genuinely useful.

Learn more: https://speedscale.com/

Business logic sits at the core of every system, yet it often becomes the hardest part to change. Rules get buried in complex code, feedback arrives too late, and even small updates turn into slow, risky efforts. This session explores why teams struggle with something that should be simple and how to bring clarity back into the process.

Testers weren’t nervous about AI replacing their work, the challenges were actually logistical. Teams struggled with unpredictable pre-production environment changes, global coordination, and unclear deployment windows, which caused confusion during monitoring and adoption. — Mush Honda, Chief Quality Architect at Katalon Follow Katalon for more insights in our series!

As AI agents take on critical roles in testing and automation, their weaknesses can become hidden risks waiting to surface. This session explores how applying red teaming techniques, borrowed from cybersecurity, can help uncover vulnerabilities in AI agents before they cause real-world failures.