Network Automation and Software-Defined Networking Questions

I would rebuild trust before I try to scale automation. First, I would run in shadow mode, which means the automation produces the proposed change but does not touch devices yet. That lets the team compare output to manual work.

For the first workflows, I would pick things that are high-volume, low-risk, and reversible, like inventory checks, config backups, or drift reports. I would avoid disruptive changes until the team sees consistent results.

To avoid a pile of ad hoc scripts, I would create one supported automation path, shared libraries, code review, tests, and named owners. I would also publish runbooks and a small set of approved workflows, then deprecate one-off scripts that duplicate the platform.

The main goal is confidence through repeatability. When people see the same inputs produce the same output, and rollback is clear, adoption usually follows.

Situation: I was on call when an automated config push changed a routing policy on a subset of edge devices, and traffic started behaving oddly.

Task: My job was to stop the impact, identify the bad change, and make sure we did not repeat the failure.

Action: I paused the rollout immediately, rolled back only the affected devices, and coordinated with operations to watch service health while the change was contained. Afterward, I led the postmortem and changed the automation flow so risky routing updates had a canary stage, a lint check, and a human approval gate before broad deployment.

Result: The issue was contained quickly, and later rollouts were much safer because the same class of mistake could no longer go straight to production.

The main lesson was that trust in automation comes from guardrails, not just faster scripts.

I would validate in layers, because different failures need different checks.

• Syntax mistake: catch it before traffic changes. Use vendor parse or commit validation in a dry-run so the device rejects bad syntax early.
• Reachability issue: verify control-plane and data-plane paths in a lab or canary. Reachability means packets can still get to critical prefixes and back.
• Behavior change at scale: use staged rollout plus traffic replay or a small canary, because some issues only appear under real load or vendor quirks.

Example: if a route policy changes a next hop, a syntax check will catch a malformed line, a reachability test will catch a blackhole to a key subnet, and a scale test may reveal slower convergence only after thousands of flows.

I would not treat the lab as production, but I would make it good enough to prove the obvious failures before the change reaches a broad blast radius.

I would design the rollout to keep the blast radius small and make every step observable.

• Concurrency control: roll by region and by small batches, with a cap on active sessions per device family.
• Pre-change checks: confirm device reachability, management-plane health, current config version, and maintenance windows.
• Health validation: after each batch, verify routing adjacency, reachability to key prefixes, and service health before continuing.
• Rollback criteria: fail fast on parser errors, control-plane instability, or any sign that the canary batch degraded service.
• Operator visibility: show batch progress, pass or fail status, recent diffs, and the exact rollback action available.

I would also require idempotent jobs, so rerunning the pipeline does not double-apply a change. A good pattern is canary, then one region at a time, then broad rollout only after the earlier batches stay healthy.

If the first batch shows a problem, the system should stop automatically and hand the operator a clear reason, not keep pushing forward.

I would keep intent and logic in Git, but leave secrets out of it.

• In version control: templates, device roles, site inventory, validation rules, and deployment code.
• Render or inject at deploy time: per-device values, generated interface stanzas, and credentials.
• Secrets should come from a secret manager, not plaintext files.

To make the result reproducible, the same commit should always render the same config. That means pinned template versions, deterministic rendering, and storing the rendered artifact or at least the exact commit SHA used for deployment. Reviewability comes from showing both the template diff and a sample rendered diff in CI.

Example: a PR changes the BGP template and a site variable file. CI renders the configs for one router from each site, and reviewers can see exactly what the device will receive before anything is pushed.