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BLOG | Sep 11, 2025

How to Adopt Agentic AI Without Adding Risk: Guarding Against Errors at Scale

Agentic AI is powerful—but without trustworthy data, it can break enterprise networks faster than it can fix them. Learn why trustworthy data is the foundation for safe adoption and how to avoid AI hallucinations at scale.
Nikhil Handigol
Nikhil Handigol Nikhil Handigol is a co-founder of Forward Networks. Nikhil holds a Ph.D from Stanford University in Computer Science. 
Who should read this post?
  • CIOs, CTOs, CISOs, and VP-level IT executives at complex enterprises evaluating agentic AI adoption
  • Leaders seeking to reduce uncertainty and risk while implementing AI-driven autonomy in large-scale, hybrid environments
  • Executives concerned about challenges such as AI hallucinations, compliance gaps, and the need for trustworthy data across the enterprise
What is covered in this content?
  • Why agentic AI built on unreliable data increases risk for complex enterprises, including AI hallucinations and large-scale failures
  • How trustworthy, detailed network data provides the foundation for safe and effective AI-driven autonomy
  • What are practical steps to reduce uncertainty and adopt agentic AI responsibly in hybrid, large-scale environments

What makes agentic AI risky for networks?

Agentic AI—the emerging class of autonomous systems that can plan, decide, and act with limited human oversight—has become one of the most talked-about technologies of 2025. The promise is enormous: faster operations, automated decision-making, and the ability to handle complexity at scale.

But in networking environments, these same capabilities can introduce serious risk. When an agent is powered by incomplete or inaccurate data, the result isn’t just a bad decision—it can be a chain reaction of errors. AI hallucinations, flawed assumptions, or missing context can lead to outages, compliance gaps, or even security incidents. Unlike traditional automation, which usually fails in predictable ways, agentic AI can break things faster and at a far greater scale.

Why does trustworthy data matter so much?

The single most important factor in safe autonomy is data. Without precise, comprehensive visibility into the network, even the smartest AI agent will make unreliable choices. For complex enterprises with hybrid and multi-cloud environments, this challenge only multiplies.

This is where a network digital twin comes in. By creating a mathematically accurate, always-up-to-date model of the network, enterprises gain a trustworthy foundation for agentic AI. Verified data reduces uncertainty, eliminates blind spots, and ensures that AI decisions are grounded in reality rather than assumptions.

How can complex enterprises adopt agentic AI safely?

Adopting agentic AI doesn’t have to mean adding unnecessary risk. The key is building on secure, validated foundations. Executives evaluating this technology should look for:

  • Trusted data sources that eliminate the chance of AI hallucinations
  • Guardrails and observability tools that provide continuous oversight
  • Clear processes for human-in-the-loop intervention when autonomy needs a checkpoint

By combining autonomy with accountability, complex enterprises can unlock the benefits of agentic AI while keeping their networks resilient and compliant.

Where can I learn more about safely deploying agentic AI in my network?

Our co-founder, Dr. Nikhil Handigol, recently published an article in AIwire titled “Avoiding Mayhem, Enabling Autonomy: A Three-Act Play on Trustworthy Agentic AI.” In it, he explains:

  • Why autonomy without trustworthy data can lead to catastrophic errors
  • How AI hallucinations and incomplete context are particularly dangerous in enterprise networks
  • What are practical steps for ensuring that agentic AI delivers on its promise while remaining safe and accountable

His perspective highlights a core truth: agentic AI is not just hype. It can deliver real value—if implemented with the right foundations. Learn more about Forward Networks’ AI features. 

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