DevOps and
automation
SDEN wires CI/CD, observability, and automation so engineering teams ship safely, repeatably, and without the manual toil that hides risk.
What this domain covers
DevOps work at SDEN targets a specific outcome: a one-command deploy that the entire team trusts. The path to that outcome is unglamorous — branch protection, mandatory code review, automated tests gating merge, infrastructure as code, deploys triggered from main, observability wired before the feature ships. None of it is interesting in isolation. The compound effect is that releases stop being events and become a default rhythm.
Observability is treated as a feature, not an afterthought. Every service emits structured logs, RED metrics (rate, errors, duration), and traces by default. Dashboards are committed to the repo (Grafana as code) so they survive the engineer who built them, and SLOs are written down so the on-call engineer knows what a real incident looks like versus a noisy alert.
DevOps and automation — the SDEN defaults
Defaults we ship
- GitHub Actions (or GitLab CI) with required status checks on protected branches
- Deploys triggered from main; preview environments per pull request
- Structured logs + RED metrics + distributed tracing on every service
- SLOs documented; alerts tied to SLO burn rate, not to host metrics
Deliverables
- CI/CD pipeline configuration committed to your repo
- Observability stack with dashboards as code
- On-call runbook for the services we operate or hand over
- Incident response template with post-mortem culture wired in
What we refuse to ship
We will not bypass tests to ship a 'quick fix.' If a hotfix needs to skip a check, the check itself is the bug — we fix the check and then ship.
DevOps & automation
questions we get asked.
Direct answers to the questions we get asked the most. If yours isn't covered, write to the team.
More from
the SDEN blog.
Cornerstone writing from the SDEN team — what AI changes, what it doesn't, and how a senior team ships the difference.
DevOps and automation: the operational layer that lets AI products ship
AI features change deploy cadence, observability needs, and incident response. The DevOps that supported a CRUD app does not survive a model-served endpoint.
AI audit for founders: what to assess before you invest more
An AI audit inventories every integration a business already runs, ranks the risk, and gives a defensible build-or-buy verdict — before the next investment.
How AI is rewriting business operations — and where it still has to earn trust
AI is moving from demo to production inside operating businesses. What changes — and what to refuse — when intelligence becomes a load-bearing part of the stack.
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