Reliability & Observability Foundations
A reliability operating model with data-driven SLO proposals, automated incident triage, self-improving postmortems, and adaptive observability.
- Data-driven SLO proposals: traffic patterns, error rates, and business impact analysed to recommend targets with automated error budget monitoring and escalation
- Automated incident first-response: alerts correlated with recent deploys and config changes, relevant traces pulled, diagnosis presented with proposed actions and runbook execution
- Self-improving postmortems and adaptive observability: incident timelines built from telemetry, contributing factors identified, remediation proposed -- OpenTelemetry baseline with automated instrumentation gap detection
Data-Driven SLOs Aligned to Business Impact
Reliability without a target is just hope. Analysis of traffic patterns, error rates, and historical incident data produces SLO targets that reflect actual user experience rather than arbitrary thresholds. Each recommendation is tied to business impact: services are classified by criticality based on traffic volume, revenue dependency, and incident history, so a payment endpoint and an internal reporting dashboard receive appropriately different targets.
Error budget burn is monitored in real time. When a budget is consumed faster than expected, automated workflows trigger escalation -- paging the right team, surfacing the relevant dashboards, and flagging whether a feature freeze is warranted. This removes ambiguity during incidents and keeps reliability decisions grounded in data.
Fry Express delivers SLOs as configuration wired into dashboards and alerting. Budget burn is visible in real time, and proposed thresholds are reviewed and approved by your engineering team before they take effect.
Incident Response and Self-Improving Playbooks
An incident response process that lives only in people's heads does not survive staff turnover or a 3 a.m. page. Agents perform first-responder triage: they correlate alerts with recent deploys, config changes, and similar past incidents, pull relevant logs and traces automatically, and present a structured diagnosis to the on-call engineer with proposed actions. The engineer decides what to do; the agent handles the execution of approved runbook steps.
Post-incident, agents draft incident timelines from observability data, identify contributing factors, and propose remediation items with clear owners. Humans review, add context, and finalise. This turns postmortems from a dreaded chore into a structured review that consistently produces actionable improvements.
Playbooks are living documents. After each incident they update automatically based on what worked and what did not -- new diagnostic steps, revised escalation paths, and refined rollback procedures. Fry Express establishes the incident workflow, the post-incident review cadence, and the integration that keeps playbooks current without manual upkeep.
Adaptive Observability With Instrumentation Gap Detection
The foundation remains an OpenTelemetry baseline capturing traces, metrics, and structured logs across your services with consistent instrumentation conventions. On top of that baseline, agents detect instrumentation gaps by analysing trace data and proposing missing spans where visibility drops off -- ensuring coverage grows with your architecture rather than lagging behind it.
Anomaly detection uses adaptive baselines that self-tune based on deploy patterns, traffic shifts, and seasonal variation. Dashboards are built around the question "what changed?" -- agents surface the most likely root cause alongside the alert so engineers move from symptom to diagnosis without switching tools.
Fry Express configures auto-instrumentation where available and provides guidance for manual instrumentation of critical code paths. Continuous validation ensures that instrumentation remains complete as services evolve, flagging gaps before they become blind spots during an incident.
These deliverables establish reliability as an intelligent, automated discipline. Agents propose targets, perform first-response triage, draft postmortems, and detect observability gaps. Humans make the decisions and own the outcomes.