A Three-Act Play on Trustworthy Agentic AI

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Act I: The Promise

The curtain rises on a typical enterprise. Its network, like most, is a patchwork of legacy infrastructure, multi-cloud deployments, misaligned configurations, and invisible dependencies. No single person truly understands it. Engineers maintain it the way you’d tend a volcano—carefully, nervously, and never with full confidence that it won’t erupt.

We’re experiencing a moment of reckoning.

Across industries, the pressure is mounting. CIOs and CISOs are being asked to move faster, do more, and respond in real time to increasingly complex threats and demands. Enter agentic AI—not just another chatbot or copilot, but a new class of intelligent system that can take action. Not suggest. Not summarize. Act.

The promise is compelling: automation without micromanagement, autonomy with intelligence. The vision? AI agents that troubleshoot issues before users notice them, optimize routing on the fly, mitigate risks proactively, and streamline change management without requiring 12 meetings and a war room.

The CIO cheers: “Finally, we can automate everything!”

The engineer whispers: “And break everything faster, too.”

The environment in which these agents are expected to operate—the enterprise network—is one of the most brittle, complex systems in modern IT. It spans physical and virtual domains, multiple clouds, legacy systems, and vendor-specific data formats. Its configurations are fragile. Dependencies are hidden. One incorrect keystroke can take down customer-facing apps, disrupt operations, or allow bad actors to breach security policies. And it happens more often than most executives want to admit.

The network is the heartbeat of the business—and it’s dangerously easy to disrupt.

This is the paradox: the systems most critical to business continuity are also the least forgiving. And now we’re asking AI to operate them.

For agentic AI to succeed here—not just in theory, but in practice—it needs more than autonomy. It needs a foundation. A way to see the system in full detail. A way to catch mistakes before they cause disasters. A way to build trust.

This is where the play begins.

Act II: The Problem

The second act opens in darkness—literally. The network has gone down after a misaligned AI-driven configuration change. A thousand modifications, made in seconds, and no one knows what went wrong. Worse, no one knows how to reverse it. There are no audit logs, no validation checks, no rollback plan. The AI doesn’t remember what it did. The engineers can’t follow the trail. The business is losing money. Fast.

Agentic AI, off-stage, shouts: “I was just trying to help!”

And it was.

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The failure wasn’t arrogance. It was ignorance. Agentic AI wasn’t the villain—it was flying blind.

Without a complete picture of the network, even the most sophisticated AI cannot succeed. And this isn’t about surface-level visibility. To make intelligent, reliable decisions, agentic AI needs an unprecedented level of detailed network context:

The relevant line of device configuration
The relevant routing policy
The relevant security rule
The relevant VLAN, VRF, and virtual appliance
The full topology across on-prem, cloud, and hybrid environments
And, most importantly, the specific path a packet can take

It needs to know not just what’s currently happening in the network, but what could happen—what should happen. Desired state. Golden configurations. Intended security architecture.

None of this lives in a log file or a metrics dashboard.

Blaming the agent is easy. But the truth is more uncomfortable: the system set it up to fail by asking it to operate in a world it couldn’t see. Incomplete, inaccurate, or outdated data was the true culprit.

That’s where context engineering becomes essential. Data alone is insufficient. What AI needs is a way to understand relationships, dependencies, and intent. Context engineering transforms disjointed telemetry and configurations into structured, semantically rich knowledge—organized in a way AI systems can act on with precision and confidence.

Here’s where most organizations miscalculate: they assume observability is enough. But logging what has happened won’t prevent disaster. Agentic AI needs a different kind of infrastructure—a behavioral model, not just a monitoring tool.

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Enter the network digital twin, stepping into the spotlight.

With detailed, accurate, normalized, vendor-agnostic data across routers, switches, firewalls, and software—across hybrid and multi-cloud environments—it delivers the foundation agentic AI needs to succeed. It doesn’t guess. It collects. It parses. It analyzes. It speaks the “language” of all the major hardware vendors and public clouds. It validates. It documents. It proves that every change aligns with business intent and doesn’t accidentally break connectivity.

The digital twin isn’t just a data aggregator—it’s the operational backbone for agentic AI context engineering. It provides a full, accurate representation of the network’s current state and behavior.

That foundation isn’t just a safety net. It’s the system that prevents disasters in the first place—by ensuring AI decisions are grounded in reality, aligned with intent, and executed within well-defined boundaries. And just in case something slips through, it also provides the forensic clarity needed to recover fast and without blame.

Act III: The Path Forward

The final act opens not in disaster, but in preparation.

The Network Engineer, no longer consumed by command-line firefighting, is finally free to think strategically.

In this new model, engineers define goals, enforce boundaries, and oversee outcomes. Each agent operates within a clearly scoped domain—troubleshooting, change validation, risk analysis—and is constrained by strict access controls: context-aware, identity-bound, and rollback-capable.

The engineer becomes not the fixer, but the architect of trust.

The Enterprise Network, no longer a black box, becomes transparent and predictable. Each device and configuration is represented in a normalized, cross-vendor model. Every packet path is understood. Every outcome, verifiable. The network, once reactive, becomes proactive.

And trust? It’s earned—through accurate context, enforced policy, and the ability to undo what the AI has done. Not because AI is infallible, but because the system is built to catch and correct its failures before they escalate.

The curtain falls. No outages. No breaches. Just a secure, self-aware system where humans and agents collaborate—each doing what they do best.

Encore: A Plan for Creating the Foundation for Safe Agentic AI

As the applause begins, the work is just starting. Deploying agentic AI isn’t about rolling out a model—it’s about building a system that understands itself well enough to let agents operate safely. That system starts with context.

Here’s how to lay the foundation:

Build a Comprehensive Network Inventory: Know what you have, down to the device, port, configuration, and connectivity. You can’t manage—or delegate—what you can’t see.
Define “Good” Data:
Establish what trustworthy, actionable data looks like in your environment. Use this as a baseline for acceptable inputs to AI systems.
Practice Context Engineering:
Normalize, correlate, and model your network data. Organize it into a machine-consumable structure that defines intent, dependencies, and business-critical paths.
Deploy a Network Digital Twin:
Use the digital twin to create and maintain a behaviorally accurate representation of your infrastructure. This becomes your AI’s reference frame for safe action.
Enforce Guardrails and Boundaries:
Clearly define what agents can and cannot do. Implement identity-bound controls, intent validation, and full auditability.
Start Small, Iterate Smart:
Apply AI to scoped use cases like config verification or incident triage. Monitor outcomes. Refine access. Expand only when trust is earned.
 Align Autonomy with Strategy:
Ensure AI agents support not just uptime, but business goals—resilience, compliance, and operational agility.
Agentic AI can only be trusted when the system it acts upon is trustworthy.
And that trust doesn’t come from the agent. It comes from the data. It comes from the context.

Agentic AI can only be trusted when the system it acts upon is trustworthy. And that trust doesn’t come from the agent. It comes from the data. It comes from the context.

About the Author

Nikhil Handigol, Co-Founder, Forward Networks, is a Computer Science PhD from Stanford. As a member of the Stanford team that pioneered SDN/OpenFlow, his research focused on using SDN principles for systematic network troubleshooting (NetSight), flexible network emulation (Mininet), and smart load-balancing (Aster*x). Previously, he worked at SDN Academy, ON.Lab, and Cisco.

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