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.

As frontier AI models continue to grow in capability, many organizations assume the largest model will always deliver the best outcomes. In reality, successful AI solutions are built through smart architecture, effective context engineering, and thoughtful model orchestration, not simply by using bigger models. Join Praveen as he shares lessons learned from building and evaluating enterprise AI solutions, including the CAVE proof of concept. Through real-world examples, he will explore how organizations can balance performance, cost, and scalability by selecting the right model for the right task, optimizing token usage, and designing efficient multi-model workflows. Whether you are building copilots, agents, or enterprise AI platforms, this session will provide practical guidance for maximizing business value while minimizing operational costs. Key takeaways: → How context engineering can dramatically improve AI performance while reducing token consumption. → When frontier models add value and when smaller models are the better choice. → Architectural patterns for building scalable, cost-effective AI solutions. → Practical lessons in model orchestration, efficiency, and enterprise AI adoption. → How to maximize intelligence per dollar spent when building production AI systems.