As AI models become more powerful, many organizations assume larger models automatically deliver better results. Our experience building enterprise AI solutions showed the opposite: architecture often matters more than model size. In this session, we share practical lessons learned from developing and evaluating AI systems, including the CAVE proof of concept. Attendees will learn how context engineering, model selection, workflow design, and multi-model orchestration can significantly reduce cost while maintaining quality. We will explore strategies for optimizing token usage, improving performance, and determining when frontier models genuinely add value versus when smaller models are the smarter choice.

As AI agents become capable of planning, reasoning, and taking action with minimal human intervention, the challenge is no longer enabling autonomy, it's governing it. How do you give agents the freedom to make decisions while ensuring they remain secure, reliable, and aligned with business objectives? Join engineering leaders as they discuss the frameworks, guardrails, and architectural patterns that enable autonomous AI systems to operate safely in production. Learn how leading teams balance autonomy with human oversight, implement effective governance, and build trust in increasingly capable agentic systems. Key takeaways → Determine where agents should operate independently and where human intervention remains essential. → Design guardrails, permissions, and governance frameworks that enable safe autonomy. → Build monitoring and oversight mechanisms that maintain visibility into agent behaviour and decision-making. → Balance innovation with security, compliance, and operational control as agent capabilities continue to evolve.