AI agents have the potential to help network teams move faster, but successful Agentic NetOps takes more than giving an AI model access to the network.
In a recent Forbes article, NetBrain CPTO Song Pang explores two challenges organizations need to solve together: reducing the human-error risks behind network downtime and keeping the cost of AI reasoning under control.
The foundation starts with context.
Agents need a continuously updated understanding of the network, clear network intents that define what the environment should look like, and guardrails that limit what the agent can access or change. Without that foundation, adding an AI agent can simply introduce another layer that still requires human oversight.
Cost is another important part of the equation. Agentic reasoning can require multiple model calls as an agent plans, checks, and revises its work. That means every task does not necessarily need the most powerful—or most expensive—model.
A more practical approach is to use deterministic automation for routine, well-understood diagnostics and reserve deeper AI reasoning for the complex exceptions that actually require judgment. Organizations can also put limits around agent loops, delegated tasks, and budgets while measuring cost against outcomes such as resolved incidents.
The takeaway: downtime reduction and AI cost control should be designed together from the start.