Practice : Observability-Driven Design

Purpose and Strategic Importance

Observability-Driven Design is the practice of designing systems with built-in telemetry from the start, enabling teams to understand system behaviour in real time. It ensures that applications are instrumented with metrics, logs, and traces that support debugging, monitoring, optimisation, and decision-making - before problems arise.

By prioritising observability as a core design concern, teams improve reliability, reduce time to recovery, and make better engineering and product decisions. It transforms operational awareness from reactive to proactive.

Description of the Practice

  • Systems emit signals that describe their internal state (metrics, logs, traces).
  • Observability is baked into design and development - not retrofitted after deployment.
  • Tools include Prometheus, Grafana, OpenTelemetry, Honeycomb, DataDog, and Splunk.
  • Dashboards, alerts, and SLOs are designed around business goals and system intent.
  • Instrumentation supports root cause analysis, anomaly detection, and performance tuning.

How to Practise It (Playbook)

1. Getting Started

  • Define what “healthy” means for your service (e.g. latency, throughput, error rate).
  • Instrument key flows with metrics (e.g. request duration, 5xx count), logs (structured), and traces (distributed).
  • Expose health checks and readiness probes to support monitoring and automation.
  • Set up a dashboard for visibility into system health during dev, test, and early deployment.

2. Scaling and Maturing

  • Use structured, context-rich logging and correlate logs with metrics and traces.
  • Adopt OpenTelemetry or similar frameworks for consistent instrumentation.
  • Define SLOs and SLIs tied to customer and business expectations.
  • Shift observability left - validate telemetry during development, not after deployment.
  • Use telemetry data in incident response, release gates, and architecture reviews.

3. Team Behaviours to Encourage

  • Treat observability as a shared responsibility across engineering.
  • Design instrumentation alongside feature development - not as an afterthought.
  • Use post-incident reviews to improve observability gaps.
  • Make data accessible and useful to product and operations teams alike.

4. Watch Out For…

  • Instrumentation that’s too noisy or lacks context - quality over quantity.
  • Tooling silos where logs, metrics, and traces are not correlated.
  • Reliance on third-party defaults - build meaningful signals for your systems.
  • Lack of ownership for dashboard, alert, or SLO hygiene.

5. Signals of Success

  • Teams resolve incidents faster with clearer insights.
  • Systems provide actionable telemetry out-of-the-box.
  • Engineering decisions are driven by real operational data.
  • Observability enables resilience, not just monitoring.
  • System health is visible, meaningful, and trusted by both engineers and stakeholders.