Agentic AI Can Help-or Harm. How a Digital Twin Keeps It in Check

Agentic AI is reshaping the enterprise software landscape with autonomous, goal-driven systems that promise operational speed, scale, and intelligence. But while the opportunities are massive, the risks are equally significant—particularly when these agents act on inaccurate, incomplete, or siloed data.

In this 45-minute discussion, Dr. Nikhil Handigol, Co-founder of Forward Networks, and Howard Holton, CTO of GigaOm, explore the critical role that network digital twins play in de-risking agentic AI adoption. They share expert insight into what’s driving the surge in agentic AI, what’s holding it back, and how enterprises can use digital twins to build trust, enforce verification, and avoid unintended consequences.

What You’ll Learn:

• Why accurate, context-rich data is essential for safe and effective agentic AI
• How a network digital twin acts as both a trustworthy data layer and a verification engine
• Key risks of autonomous agents in enterprise networks—and how to mitigate them
• The difference between monitoring tools and behaviorally accurate digital twins
• Real-world use cases where digital twins prevent outages, accelerate response, and support AI-driven automation
• How enterprises can prepare their infrastructure, data, and teams for agentic AI

Who Should Watch:

• CIOs, CISOs, and technology executives evaluating AI adoption strategies
• Network and security architects looking to modernize their data infrastructure
• Enterprise AI/ML teams working on safe deployment of autonomous agents
• IT leaders concerned with governance, verification, and operational risk in AI systems