AI Agents for Network Automation: Just Another Tool in Your Stack

I was sitting down with Ethan Banks at AutoCon 5 recently for an episode of Packet Pushers’ Heavy Networking, and we got into a conversation I’ve been having a lot lately. Every time agentic AI comes up in a room full of network engineers, the energy shifts. And usually, the engineers who’ve been doing this the longest are the ones who are most skeptical.

That skepticism is earned. But I think it’s pointed at the wrong thing.

The way we think about it is this. AI agents are not here to replace the automation you’ve spent years building. They don’t retire your pipelines, your Python scripts, or your orchestration workflows. They’re a new capability, a powerful one, that slots into the stack you already have. Once you see it that way, most of the anxiety disappears.

Here’s how I actually think about it.

What an Agent Actually Is

A lot of the confusion starts with the word “agent” itself. It implies something autonomous, unpredictable, maybe even a little dangerous. So let’s strip it back.

An agent is a software process. You give it an LLM for reasoning, a prompt that defines what it’s trying to achieve, tools it’s allowed to use, and skills, which is the domain knowledge about how to operate in your environment. That combination is what produces useful, bounded outcomes. If I give it three tools, say a ping, a trace route, and the ability to notify me on Slack, those are the only three things it can do.

That’s the part the word “agent” obscures. It isn’t roaming freely through your infrastructure making decisions. You’ve defined the scope. The LLM reasons within that scope to figure out what to do next, the same way a senior engineer would when something goes wrong and the runbook doesn’t have an answer.

Where LLMs are genuinely strong is reasoning at a high level. They understand what a software upgrade involves. They understand BGP and OSPF. They can read a method of procedure written in plain English and follow it. Where they’re not as strong is version-specific config generation for your mixed Juniper and Arista fabric. That part is still yours to own.

The balance of determinism and reasoning is the whole game, and knowing where each belongs is what makes this work.

Reasoning Lives Where Determinism Can’t

If you’ve been building network automation for any length of time, you know this problem intimately.

Every time a different error condition shows up, whether it’s a device offline, an unexpected API response, or a reroute you didn’t plan for, you write a module. You handle that exception. You add logic to the script. Over time you accumulate thousands of lines of code that exist purely to handle the long tail of things that shouldn’t happen but always do. The tools we’ve had have relegated us to coding every exception.

Deterministic automation is non-negotiable. For a given set of inputs, you need a predictable output. That’s not changing. But determinism also means that when conditions fall outside what you coded for, the pipeline hard-fails. And most of us don’t operate in perfect standardized environments with clean data and networks that are never offline.

That’s where reasoning earns its place. Not by replacing the pipeline, but by handling what the pipeline can’t. Instead of writing exception handlers for every variant of an error message, you tell the agent in plain English. If the device is offline, use a backoff algorithm. If you see these conditions, they’re acceptable. If you see those, stop and page me. It’s helping me work through what we would normally have done on a bridge call at two in the morning.

That’s the technical debt reduction the industry has been looking for. Not magic, just the right tool applied to the right part of the problem.

Governance Is the Architecture, Not the Afterthought

This is where I see engineers get stuck. They’ve heard about open agent frameworks that pick tools at runtime, an agent with broad access that starts doing things nobody planned for. That’s a real concern, and it’s exactly why we built FlowAI the way we did.

Tools are defined at build time. Whoever designs the agent decides what it has access to, the same way you decide what a workflow can do. If you give it read-only access, it can only have read-only outcomes. If you don’t give it access to your network controller, it can’t touch your network controller. Every reasoning step, every action taken, is fully auditable.

This is the key distinction between agents you can trust on production infrastructure and agents you can’t. The governance your team has already built, including RBAC, audit trails, and approval gates, doesn’t get rebuilt for AI. It’s already there. The agent runs inside it.

Start read-only. Watch the outcomes. Expand the scope as confidence grows. It’s the same way you’ve always earned trust in automation, and it still works.

You’ve Been Here Before

Ten years ago this industry had a nearly identical conversation about network automation.

What if it pushes the wrong config? What’s the blast radius? I don’t fully understand how it makes its decisions. I’m not ready to trust it.

The answer then was the same as it is now. Human in the loop, read-only, diff sets, bounded use cases, and gradually extended trust as the system proved itself. Today, most infrastructure-as-code pipelines run autonomously and notify when done. We got through that trust cycle because early adopters built the confidence, and the rest of the industry followed. I get a little deja vu thinking about it, because if you circle back ten years, it was a very similar concept.

We’re at the start of that same cycle with agentic AI. The teams running agents in production today are starting read-only, with information gathering, ticket triage, and compliance checks. Low blast radius, high value, immediately useful. Over time, as confidence builds, they’ll delegate more.

The maturity model is the same. The difference is we’ve already been through it once, so we know how it ends.

So Where Do You Start?

If you’ve been building network automation with scripts, playbooks, and orchestration workflows, you’re further along than you think. Agents run on top of what you’ve already built. Your Python scripts become tools. Your orchestration platform becomes the governed environment where agents operate. Nothing gets thrown away.

The questions worth asking aren’t “should I do this?” They’re these. Where does human judgment currently fill gaps that automation can’t? Where do my pipelines hard-fail when conditions change? Which operational tasks never hit the ROI threshold for full automation but still eat engineering time every week?

Those are the places where a reasoning layer changes the calculus. Agentic AI is powerful enough that it creates different dynamics in how we think about it. But if we can put it in the right bucket and treat it as one more tool to add to the toolkit, in whatever way makes sense to get to better outcomes, that’s what gets us there.

AI agents are just another tool. A new one, with real capability we didn’t have before. But the way you adopt it, carefully, with governance, read-only first, and earned trust over time, that part you already know how to do.

Listen to the Full Conversation

I sat down with Ethan Banks on Packet Pushers’ Heavy Networking at AutoCon 5 to dig into all of this. The full episode is below. Agree, disagree, or have questions, let me know on LinkedIn.