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Governing AI Agents Before They Govern You

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Collibra
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What to expect

As AI agents increasingly operate autonomously within enterprise environments—making decisions, triggering workflows, and integrating across systems—organisations face a critical challenge: how do you govern intelligent systems that act independently? This session at Tech Show Frankfurt tackles the governance gap that emerges when autonomous AI moves from controlled lab environments to production-scale deployment.

Ed Atkins, Senior Customer Engineer at Collibra, delivers this focused 25-minute session exploring the intersection of AI governance, transparency, and enterprise risk management. The session examines how organisations can establish robust governance frameworks for agentic AI systems before these autonomous actors create compliance and trust issues that outweigh their value.

Why AI Agent Governance Matters

Many enterprises are discovering that autonomous AI agents introduce hidden risks alongside their operational benefits. When AI systems can independently trigger actions, access sensitive data, and make consequential decisions without human oversight, traditional governance models break down. The session addresses three critical governance challenges:

  • Hidden Activities: AI agents often operate across multiple systems with limited visibility into their decision-making processes
  • Limited Transparency: Understanding why an agent took a specific action becomes increasingly difficult as autonomy increases
  • Diffuse Accountability: When things go wrong, pinpointing responsibility across human teams and autonomous systems proves complex

Industrial-Grade DataOps & MLOps

This session sits within the Agentic Analytics track focused on "Industrial-Grade DataOps & MLOps: From Lab to Live Deployment." Attendees will learn how to bridge the gap between experimental AI development and production deployment through governance frameworks that scale with autonomous systems.

The presentation explores practical approaches for implementing AI governance within existing DataOps and MLOps pipelines, ensuring that organisations can deploy autonomous agents confidently while maintaining compliance, auditability, and control. This is particularly relevant for data engineers, ML engineers, and enterprise architects responsible for operationalising AI systems at scale.

Who Should Attend

This session is designed for:

  • Chief Data Officers and AI governan

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