What resilience patterns (e.g., circuit breakers, fallbacks, human-in-the-loop overrides) should be built into agentic AI infrastructure?
How will agentic AI reshape the role of enterprise architecture itself—moving from static blueprints to adaptive, self-optimizing systems?
What “red flags” should architects look for that indicate an agentic system is not enterprise-ready yet?
What do you see as the biggest challenge in moving from task-based AI assistants to fully agentic systems capable of pursuing enterprise-wide goals?
How do we prevent agentic AI from over-optimizing for speed at the cost of test accuracy?
How do we design agentic AI so that it balances autonomous optimization (like cost savings) with enterprise guarantees (like ethics, compliance, and risk management)?
In the context of QA and testing, how can agentic AI move beyond just autonomous test generation to actually providing enterprise-grade guarantees of software quality, reliability, and compliance at scale?
What’s the biggest challenge in scaling from AI assistants to fully agentic systems?
How do you benchmark performance when success criteria are dynamic and agent-driven?
How do you integrate agentic AI into legacy systems without breaking stability?
Do federated multi-enterprise agent ecosystems require a new architectural blueprint?
How should enterprise architecture evolve to support agentic AI systems that operate with autonomous goals?
With Agentic AI, what are your thoughts on a central QA/QE agent for the team versus an agent for each team member individually (more individual style)? Is there any benefit to having a central agent with each team member having the style file?
With AI tools now automating many testing tasks, how relevant is it to still learn traditional automation frameworks like Selenium or Rest Assured? Should QA professionals focus more on AI-driven testing, or balance both, and if so, how?
How do you architect zero-trust security for autonomous AI agents communicating across enterprise ecosystems?
What does the roadmap look like for evolving from autonomous goals to enterprise-grade guarantees?
As agentic AI shifts from autonomous goal-seeking to delivering enterprise-grade guarantees, what guardrails or frameworks do you see as most critical to ensure trust, accountability, and business value at scale?
Does agentic AI hamper engineers’ capabilities to troubleshoot, try different solutions and develop frameworks?
Do you see architectures shifting towards self-healing/self-optimizing models where agents redesign workflows themselves?
How do you envision enterprises measuring the ROI of agentic AI—beyond productivity gains, especially in terms of trust, compliance, and long-term adaptability?