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
The target is one thing: a one-command deploy the whole team trusts. Getting there is unglamorous: branch protection, mandatory review, tests gating merge, infrastructure as code, deploys off main.
None of it is interesting alone. Together it turns releases from events into a steady rhythm.
Observability is a feature, not an afterthought. Every service emits structured logs, RED metrics (rate, errors, duration), and traces by default. Dashboards live in the repo as code, and SLOs are written down so on-call can tell a real incident from 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.
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.
RAG for business: building knowledge assistants that actually work
Retrieval-augmented generation grounds AI answers in your data. What RAG is, when it beats fine-tuning or a plain prompt, and what separates a knowledge assistant you can trust from a demo.
AI agents for business: where they work, and where a workflow wins
Agents are powerful and easy to get wrong. When a task genuinely needs an agent, when a plain workflow is the better answer, and how to keep an agent safe and affordable in production.
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