Start from the failure you cannot afford
Networks are unforgiving. A confident but wrong configuration change can take a site offline in seconds, and the consequences are physical: stalled operations, blocked payments, safety systems without connectivity. Any conversation about AI in network operations has to start there — not with what the model can generate, but with what must never happen.
That framing leads to a simple architecture principle we apply in everything we build: deterministic logic makes changes; AI assists people who decide.
What deterministic automation should own
Most operational toil is repetitive and rule-based. It does not need a model — it needs well-tested code with API, SSH and SNMP access to the estate:
- Configuration generation from a validated source of truth (IPAM, inventory, design intent).
- Pre- and post-change validation — reachability, routing tables, fabric health, policy compliance.
- Scheduled collection of topology, inventory and lifecycle data into structured records.
- Drift detection: what is running versus what the design says should be running.
Where AI genuinely adds value
Once the estate is described in structured data, AI becomes useful for the work that is hard to encode as rules:
- Triage: correlating alerts, logs and recent changes into a plain-language hypothesis for the on-call engineer.
- Fault analysis: reading verbose fabric or device output and surfacing the two lines that matter.
- Design review assistance: checking a proposed change against documented standards and known anti-patterns.
- Knowledge retrieval: answering 'how is site X connected and why' from your own documentation and data — not from the internet.
Controls that make it safe
AI output in our tooling is advisory by design. Recommendations are presented with the evidence behind them, and any resulting change runs through the same deterministic validation and human approval as a manual change. Models operate on your private data with clear boundaries on what they can read, and everything they produce is logged.
This is less glamorous than an autonomous network, and considerably more useful. Engineers get their time back on analysis and troubleshooting; the organisation keeps accountable control over change.