Summary
- Autonomous networking doesn’t start with AI. Instead, it starts with knowing what is in your network and how it should work.
- Network automation is only as reliable as the inventory and operational data behind it.
- AI-assisted network automation can help teams analyze issues and recommend next steps, but production changes still need controls.
- Before self-healing networks can act safely, teams need tested workflows, clear guardrails, and confirmation that each change worked.
- Start with the gaps creating the most risk or manual effort, then expand automation as your team builds confidence.
Why the foundation matters more than the finish line
I’ve spent many years on the mats in wrestling and Taekwondo rooms, and nobody hands you a black belt. You earn it one repetition at a time. You practice every takedown, kata, and attack combination until it stops being instruction and becomes instinct.
Network automation follows the same discipline. Autonomous networking is the black belt everyone wants to discuss, but it rests on unglamorous white-belt work: discovery, validation, and foundational practices repeated until they become routine. Skip the kata, and you do not get a shortcut to autonomy; you get a network that looks disciplined in a demo and falls apart under pressure.
Networks are now programmable infrastructure. Yet many teams still operate them like it is 2008: SSH sessions, tribal knowledge, and spreadsheets standing in for a source of truth. The readiness gap between what is technically possible and what is operationally trustworthy is where the journey begins.
Why network automation initiatives fail before they start
Before talking about autonomy, it’s worth exploring why so many network automation efforts stall:
- Bad inventory leads to bad automation: You can’t automate a network you don’t accurately understand.
- Automation does not fix a broken process: Automation exposes a flawed manual process at scale rather than repairing it.
- One person can’t carry the program: A team cannot treat network automation as one person’s side project or resist the engineering practices needed to sustain it.
- Expectations matter and results take time: Leadership may want instant ROI, but the reality is that operational maturity takes time.
These failure modes share a root cause: a lack of trust. Teams will not allow automation to make increasingly consequential decisions until they trust the data, the intended state, the automation, the validation, and the guardrails.
That starts with defining the network’s intended state: golden configurations, hardening standards, and expected peering and routing behavior. Without an agreed baseline, compliance becomes a pre-audit fire drill instead of an operating discipline. Mature teams run a continuous cycle: define the intended state, assess the network, remediate drift, and assess again. Each successful loop earns the trust required for the next level of automation.
The unglamorous prerequisite to autonomous networking
Nobody gets excited about inventory data. But every network automation initiative that scales beyond a handful of scripts stands on clean, structured, continuously updated information about the network.
This is where discovery, source-of-truth, and compliance platforms become foundational. Tools such as Slurp’it and Netpicker from Flock9 can turn multivendor network sprawl into structured data that downstream automation—and eventually AI—can trust. The products are examples; the requirement is reliable data.
I have seen this in almost every assessment. During one acquisition project, I reviewed a customer’s spreadsheet inventory and found three naming conventions, devices decommissioned two years earlier still listed as in service, and nobody willing to bet their job on its accuracy.
Key point: You can’t script your way out of not knowing what you own.
Once the data is clean and continuously discovered—what I call a source of truth on steroids—everything downstream becomes easier: compliance, remediation, and AI. Slurp’it and Netpicker fit this Essentials stage because they help reveal drift and give engineers a lower-code path into repeatable automation. Their value is not a flashy AI story; it is the trustworthy foundation beneath one.
The pushback I hear most is not, “We don’t have the budget.” It is, “Can’t we skip to the AI part?” A polished agentic AI demo can make the finish line look close, but AI on bad data simply makes bad decisions faster. The Essentials stage is not a detour from the AI investment, but rather the work that makes the investment trustworthy.
Where AI fits in the automation stack
AI is a reasoning layer, not a replacement for mature, API-accessible automation platforms. It can:
- Interpret intent: Turn a natural-language question into a live compliance check.
- Expedite engineering: Help with parsing, TextFSM generation, templates, and script scaffolding.
- Reduce alert noise: Correlate telemetry into fewer, more meaningful signals.
AI should not replace source-of-truth discipline, guardrails, approvals, auditability, or the validated execution layer beneath it. Those controls are part of the architecture, not temporary limits that disappear when the models improve.
Key point: AI is the reasoning layer; mature automation platforms are the execution layer.
I have built this model in my own lab: Claude Code Desktop connects through MCP servers to Containerlabs, NetBox, Grafana, LibreNMS, Slurp’it, Netpicker, GitLab, Microsoft Teams, and Webex Teams. I can ask one natural-language question and have the system gather data, check compliance, and draft a remediation. It does not push changes on its own. Every fix requires human approval before it touches production. That’s the model I want: AI accelerates the thinking while people and governed automation platforms own the doing and accountability.
The maturity path to autonomy
Autonomy is earned by building trust in sequence. Each stage proves the controls needed to support the next:
| Stage | Primary focus | Trust earned |
| 1. Essentials | Discover and understand. | Inventory and topology are reliable. |
| 2. Enhanced | Standardize and validate. | Intended state is explicit; drift is visible. |
| 3. Advanced | Automate and remediate. | Workflows execute safely and prove outcomes. |
| 4. AI-assisted | Reason and recommend. | AI stays within approved data, tools, and controls. |
| 5. Closed-loop autonomy | Execute within guardrails. | Loops act and verify outcomes within policy. |
The prerequisites for autonomous networking
Before using the word autonomous, look for five things:
- A trusted source of truth: Structured, multi-vendor, continuously synchronized inventory and topology data.
- A defined intended state: Standards that are explicit, testable, and owned.
- Validated, repeatable automation: Processes understood and fixed before they are automated.
- Closed feedback loops: Continuous validation and compliance, with outcomes checked after every action.
- Governance and organizational ownership: Approvals, audit trails, policy guardrails, and a team (not a lone automation specialist) accountable for the capability.
If one of these pieces is missing, that is the place to focus. Adding AI will not make the gap disappear. However, it will make the consequences harder to predict.
To achieve autonomous networking, start where you are
Autonomous networking isn’t a product you buy. Like a black belt in martial arts, it’s a maturity level you earn and a capability you build. Skipping straight to AI-driven, self-healing networks without solving inventory, process, governance, and cultural readiness is how automation initiatives fail, not how they succeed.
My advice is simple: start where you are, not where the vendor pitch wants you to be. If you do not have a trusted source of truth, make that your goal before an AI rollout. If compliance is a one-time check instead of a continuous loop, fix the loop before adding another tool.
Need help getting started? A focused review of your environment with GDT experts can help your team identify the operational gaps, modernization priorities, and the most important next steps. Reach out to request a complimentary network automation workshop.
