What’s the balance between giving AI agents enough autonomy to be useful and keeping them under control? This session explores that tension through a practical experiment: deploying the same AI agent across progressively more constrained environments and observing how its behavior changes.
Priya walks through three stages:
Unrestricted deployment — watching how an unconstrained agent explores and behaves
Tightened constraints — limiting environment, permissions, or resources and analyzing the impact
Minimal guardrails — identifying the smallest set of controls needed to ensure safety, reproducibility, and reliability without killing creativity
Through concrete examples, attendees will discover:
How much freedom an agent actually needs to stay useful
How far you can lock things down before losing value
Where the real control points live — and why control is about *channeling* creativity, not caging it
This talk blends infrastructure, DevOps, and AI agent design to show how deployment environments shape agent behavior as much as model architecture.
Target Audience:
Cloud engineers
DevOps engineers
Infrastructure engineers
AI practitioners