concept#DevOps#Observability#Platform#Reliability

Operations

Overview of activities and practices for maintaining, monitoring, and evolving IT services and infrastructure.

Operations encompasses the organizational, technical, and procedural activities to maintain, monitor, and evolve IT services.

Maturity

Established

Cognitive loadHigh

Classification

ComplexityMedium
Impact areaOrganizational
Decision typeOrganizational
Organizational maturityIntermediate

Technical context

Integrations

CI/CD systems (e.g. GitHub Actions, GitLab CI)Monitoring tools (e.g. Prometheus, Grafana)Cloud providers and platforms (e.g. Kubernetes)

Principles & goals

Principles

Automate repeatable processesDefine measurable service levels (SLOs)Blameless postmortems and continuous improvement

Value stream stage

Run

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Over‑automation can create complexity and opacity
Missing SLOs lead to inconsistent priorities
Insufficient documentation increases MTTD/MTTR

Best practices

Small, tested releases (canary/blue‑green)
Blameless postmortems with clear action items
SLO‑driven prioritization of work

I/O & resources

Inputs

Monitoring data and telemetry
Service level objectives (SLOs) and SLAs
Automated CI/CD pipelines

Outputs

Operational documentation and runbooks
Monitoring dashboards and alerts
Postmortems and improvement actions

Resources

Description

Operations encompasses the organizational, technical, and procedural activities to maintain, monitor, and evolve IT services. It covers incident response, release management, capacity planning, and infrastructure automation to ensure availability and stability. Operations emphasizes automation, measurable service levels, and continuous improvement across development and operations boundaries.

✔Benefits

Higher availability and stability of services
Faster incident response and reduced downtime
Better predictability through capacity and cost control

✖Limitations

Requires organizational alignment and responsibilities
Initial effort for automation and observability
Not all legacy systems are easy to automate

Trade-offs

Metrics

MTTR
Mean time to restore service after failures.
Availability (uptime)
Percentage of time a service is available.
SLO attainment rate
Proportion of time defined SLOs are met.

Examples & implementations

SRE approach at a payment provider

Establishing SLOs, error budget policies and on‑call rotations to improve availability.

Automated rollout on Kubernetes

CI/CD pipeline with canary deployments, automated health checks and rollbacks.

Incident postmortem in a SaaS startup

Establish a blameless postmortem culture and derive preventive actions.

Implementation steps

Introduce basic monitoring and telemetry

Define runbooks, SLAs/SLOs and on‑call processes

Gradually automate critical operations

⚠️ Technical debt & bottlenecks

Technical debt

Non‑automated deployments
Missing structured logs and traces
Outdated operational documentation

Known bottlenecks

Legacy infrastructureInsufficient observabilityMissing automation

Misuse examples

Introducing automation without monitoring
Setting SLOs but not measuring them
On‑call roles without sufficient training

Typical traps

Focusing only on tools instead of processes and culture
Excessive optimization pressure without error budgets
Ignoring costs in scaling decisions

Required skills

Systems and infrastructure knowledgeMonitoring and observability skillsAutomation and scripting

Architectural drivers

Availability and resilienceFast recoverability (MTTR)Scalability and capacity planning

Constraints

• Regulatory requirements and compliance
• Budget and staffing limits
• Technical legacy constraints