Ask most network engineers how they feel about "AI making decisions on my network," and you'll get the same reaction people have to self-driving cars: intrigued, but not ready to hand over the wheel. That instinct is fair. Self-driving cars demand total trust up front, with no visibility into how decisions are made and no way to intervene if something goes wrong.
Probabilistic automation doesn't have to work that way — and in network operations, it shouldn't. In a recent article published in Network World on October 5, BackBox VP of Product and Engineering Richard Phillips discusses why combining probabilistic reasoning and deterministic execution helps teams manage complex networks.
What probabilistic automation is
Probabilistic automation is the ability of a system to reason, plan, and execute multi-step actions autonomously, but within governance boundaries the team defines. Think of it as conditional autonomy: AI draws on context, prior experience, and a library of proven automations to determine which steps apply to a given situation, then chains them together into a broader process.
This differs from the deterministic automation most NetOps teams already rely on: predefined, atomic tasks such as backing up a device fleet or pushing an OS update. These are reliable because they're scripted and repeatable. But they don't scale well to messier, more dynamic problems, such as determining which of thousands of new CVEs apply to your specific devices and configurations. That's where combining probabilistic reasoning with deterministic execution starts to pay off: AI handles the reasoning and adaptation, while the underlying automation library keeps execution consistent and testable.
Why the details matter
The reason probabilistic automation feels risky isn't the AI; it's the lack of visibility. Every network has its own standard operating procedures, device quirks, and tolerance for downtime. Trust comes from being able to see each step, validate it against your own practices, set exception-based alerts, and test automations in a lab before they touch production.
That's the model worth demanding: transparent, auditable, human-in-the-loop automation, not a black box you either trust completely or not at all. Humans remain accountable for outcomes; AI serves as an assistant, not a decision-maker.
Where BackBox fits in
This is precisely the problem BackBox's AI capabilities, built into Kilter AI, are designed to solve. Rather than asking NetOps teams to take AI on faith, Kilter is built on validated data, context, and best practices, providing teams with trustworthy insights, recommendations, and automation creation assistance without sacrificing control.
In practice, that looks like:
- Cybersecurity risk management: Kilter cuts through noisy, disorganized vulnerability reports by verifying which CVEs apply to your devices, identifying available workarounds, and recommending upgrade paths, turning a process that used to take weeks into one that takes hours.
- Automation creation at scale: Instead of building a separate automation framework for each vendor, teams can use Kilter as an informed assistant to translate CLI input into production-ready chains, without requiring deep scripting expertise.
- Multi-vendor risk management: Kilter checks configurations across your entire device fleet, including across vendors, and flags compliance issues so a problem discovered on one device doesn't quietly appear on ten others.
Across the board, the design principle remains the same: AI as a trusted advisor, not the sole decision-maker. Teams validate each step, set their own checkpoints, and test before anything goes into production.
Self-driving cars ask you to hand over control and hope for the best. Probabilistic automation, done right, asks for something different: give it defined boundaries, maintain visibility into every step, and let it help you move at the speed your network needs. That's a much easier leap, and with Kilter, it's one NetOps teams don't have to take blind.
Read the full article or discover how Kilter leverages AI to enable NetOps teams to automate network device lifecycle management and security operations; visit our platform page. Ready to get started? Request a demo to see Kilter in action.