Agents can already reason, plan, and act. The harder question is how you stay in control.

Agentic AI is no longer theoretical. Systems that reason, plan, and take action across workflows are moving into production environments, and they bring a new operational reality with them. The question is no longer whether AI can act autonomously, but how your organization stays accountable when it does.

This session goes past static guardrails. The panel digs into designing governance that adapts, instrumenting observability that surfaces intent and intermediate reasoning rather than just final outputs, and keeping meaningful human oversight in the loop without slowing your teams down. If your organization is experimenting with agents, or getting ready to move from pilots to production. This hour will help you build systems that are capable and controllable.

What you'll learn

• Governance for autonomous systems: how to design policy layers, permissioning models, and approval workflows that match agent behavior to your risk tolerance and regulatory requirements.
• Observability beyond outputs: why logging final responses isn't enough, and how to instrument reasoning traces, tool usage, and memory state for real transparency into agent decisions.
• Human-in-the-loop by design: practical patterns for escalation, intervention, and override that preserve human authority while letting agents work efficiently across complex workflows.
• From pilot to production control: the architectural pieces (modular workflows, sandboxing, audit trails, feedback loops) that make agentic systems safe and sustainable at enterprise scale.

Our own view, shared in the session: governance should be enforced by the environment itself, not by individual users. And agents' work should stay in code wherever possible, where it can be reviewed, versioned, and reproduced like everything else your team ships.