Big Data ATL

Agentic AI is no longer theoretical. Systems that can reason, plan, and take action across workflows are entering production environments—and with them comes a new operational reality. The question is no longer can AI act autonomously, but how do we stay in control when it does?

This webinar focuses on the practical mechanics of deploying agentic AI responsibly. We’ll examine how to design governance frameworks that go beyond static guardrails, implement observability that surfaces intent and intermediate reasoning, and maintain meaningful human-in-the-loop oversight without slowing down innovation. From dynamic policy enforcement to real-time monitoring and escalation paths, we’ll explore how enterprises can operationalize autonomy without sacrificing accountability.

If your organization is experimenting with AI agents—or preparing to move from pilots to production—this session will help you build systems that are not only capable, but controllable.

What You'll Learn:

1️⃣ Governance for Autonomous Systems: How to design policy layers, permissioning models, and approval workflows that align agent behavior with organizational risk tolerance and regulatory requirements.

2️⃣ Observability Beyond Outputs: Why logging final responses isn’t enough—and how to instrument reasoning traces, tool usage, and memory state to create transparency in agent decision-making.

3️⃣ Human-in-the-Loop by Design: Practical patterns for escalation, intervention, and override that preserve human authority while enabling agents to operate efficiently across complex workflows.

4️⃣ From Pilot to Production Control: The architectural considerations—modular workflows, sandboxing, audit trails, and feedback loops—that make agentic systems safe and sustainable at enterprise scale.

As organizations accelerate AI and analytics initiatives, one challenge keeps surfacing: enterprise data is only as useful as the context behind it.

Business definitions, ownership, metadata, lineage, and data quality signals all play a critical role in whether teams can trust, govern, and activate their data effectively. But in many organizations, that context is incomplete, outdated, or scattered across systems. As a result, governance programs struggle to keep pace, data catalogs lose relevance, and AI initiatives face greater risk around accuracy, reliability, and accountability.

This panel discussion will explore how data leaders are rethinking governance and stewardship for the AI era. Panelists will discuss why traditional, manual approaches no longer scale, how trusted business context improves analytics and AI outcomes, and where automation can help teams keep governance programs current without removing human oversight.

Join us for a practical conversation on what it takes to build AI-ready data foundations — and how organizations can move from static documentation to living, trusted context that supports better decisions, stronger governance, and more reliable AI.

In this session, we’ll discuss:

• Why business context is becoming essential to AI and analytics success
• How stale metadata and unclear ownership create governance and AI risk
• What scalable data stewardship looks like in modern enterprises
• How AI can support governance workflows without replacing human judgment
• Practical ways to keep data assets trusted, documented, and ready for use