Systems | Development | Analytics | API | Testing

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.

Agentic code generation has dramatically accelerated development, but QA often remains slowed by coordination gaps. Manual planning, delayed handoffs, and fragmented reviews continue to create friction, while AI agents operate in isolation without orchestration or governance. This session explores how to compose these siloed agents into a unified, AI-powered STLC where workflows are dependency-aware, auditable, and automatically triggered as soon as prerequisites are met.

AI has transformed how software is built and tested, yet many teams still rely on traditional metrics like pass rates, coverage, and defect counts. While these numbers look good on dashboards, they often fail to answer the most important question in the AI era. Can we actually trust what the system is doing?

Learn more at: speedscale.com.