AI-native networking vs. traditional network automation
Most engineers agree that no methodology fits every part of a network. Before choosing rule-based automation or learning-based tools, admins must consider network structure.
Automation in enterprise networks is nothing new. Automation tools, scripts and playbooks have been common practice for decades, but AI technology has largely taken over the automation conversation.
The questionable credibility of the hype surrounding AI, and the risk of major outages when delegating control to a relatively new technology, give IT leaders reason to wonder if the developments improve on traditional measures.
How traditional automation and AI-native networking differ
Both traditionally automated and AI-native network technologies aim to move packets with less human intervention. The real difference is how they make network decisions.
With traditional automation, network administrators rely on rule-based techniques they have manually created through policies, templates, scripts and playbooks. Automation relies on conditional situations, baseline thresholds and pre-defined rules to make decisions. As a result, the network becomes highly deterministic. At the same time, it requires tremendous manual upkeep and is limited to making only the decisions policies and scripts permit.
Alternatively, AI-native networking is learning-based. In this sense, logic is no longer conditional and static, but is based on inference. It can modify decision-making behavior based on real time and historical patterns using information it collects and analyzes.
From an administration perspective, the role of a network admin shifts when moving from traditional automation to AI-native. In traditional automation, admins create the logic the system uses. In AI-native networking, admins use a system that can have its own logic, but is driven by guardrails, high-risk change-approval processes, model tuning and correction when needed.
Strengths, limitations and uses
As it stands today, traditional automation holds the advantage when control and predictability are critical. If a change is known and highly repeatable, it's simple for an administrator to write a script or playbook to execute that exact rule over and over. This is where standardization of network buildouts becomes critical.However, anyone who has ever designed, built or managed a network knows that no two networks –or even a part of a network—are built and configured the exact same way. There are almost always nuances that create differences. These differences limit the ability to build traditional automation processes at scale.
As it stands today, traditional automation holds the advantage when control and predictability are critical.
That's why AI-native networking is getting so much attention. With AI-native, the focus is on the network's desired state, not hard-and-fast rulesets or policies that can break when the environment veers even slightly from what is expected. It's in these non-standardized environments where AI can use its own logic and pattern recognition to automate the path toward a desired outcome, which is an advantage.
In real-world scenarios, the advantages and disadvantages of each are rarely all-or-nothing. The same network professionals often use both traditional automation, for cases where the network must do the same thing every time, and AI-native capabilities, for cases where it must identify and react to something unanticipated.
AIOps vs. agentic systems
The topic of AIOps and agentic systems often comes up when discussing traditional automation and AI-native networking. While the two are similar, they are not synonymous.
AIOps is primarily an analysis layer. It correlates events, reduces noise and suggests likely causes across the tools the user already runs. It can sit on top of either a traditional automation platform or an AI-native platform. Regardless of the platform used, the network still waits for a human or a playbook to act on the recommendations.
On the other hand, agentic systems tend to go a few steps beyond recommendations. These systems plan and execute multi-step processes to act on recommendations and validate the results. In the context of network operations, this can mean an agent that opens a change, validates it and rolls back if needed.
However, neither AIOps nor agentic labels describe how the network itself was built. For example, an agent can execute a static playbook while an AIOps service can analyze an AI-native platform. What matters instead is what the system is able to decide, what it still needs human approval for, and whether the layer underneath still follows authored rules or updates its behavior with continuously updated models.
Andrew Froehlich is founder of InfraMomentum, an enterprise IT research and analyst firm, and president of West Gate Networks, an IT consulting company. He has been involved in enterprise IT for more than 20 years.
