You Can't Hire Your Way to Reliability
I've seen companies hire 5 SREs and expect reliability to magically improve. It doesn't. Reliability is a cultural outcome, not a headcount metric.
The Reliability Maturity Model
Level 1: Reactive
"Things break, we fix them."
No SLOs, no error budgets, post-mortems are optional.
Level 2: Aware
"We know what's breaking and how often."
Basic SLOs defined, post-mortems happen, on-call exists.
Level 3: Proactive
"We prevent most issues before they happen."
Error budgets enforced, chaos engineering started,
automated remediation for common issues.
Level 4: Predictive
"We predict and prevent issues we haven't seen yet."
ML-driven anomaly detection, capacity planning,
reliability is a product feature.
Level 5: Systemic
"Reliability is embedded in everything we do."
Every engineer thinks about reliability, every design
doc includes failure modes, every feature has SLOs.
Most companies are at Level 1-2. Getting to Level 3 is the biggest jump.
The Three Pillars of Reliability Culture
Pillar 1: Ownership
Reliability is not the SRE team's job. It's everyone's job.
ownership_model:
development_teams:
- Write SLOs for their services
- On-call for their services (with SRE backup)
- Fix production issues in their domain
- Include failure modes in design docs
sre_team:
- Build reliability infrastructure (monitoring, alerting, CI/CD)
- Consult on architecture for reliability
- Run chaos engineering program
- Manage cross-cutting reliability projects
- Train development teams on SRE practices
Pillar 2: Learning
Every incident is a learning opportunity. But only if you structure it:
def post_incident_learning(incident):
# 1. Blameless post-mortem (within 48 hours)
postmortem = write_postmortem(incident)
# 2. Share with entire engineering org
post_to_engineering_channel(postmortem.summary)
# 3. Add to searchable incident database
incident_db.insert(postmortem)
# 4. Extract patterns
similar = incident_db.find_similar(postmortem)
if len(similar) >= 3:
create_reliability_project(
title=f"Systemic issue: {postmortem.category}",
evidence=similar,
priority="high"
)
# 5. Update runbooks
if postmortem.new_knowledge:
update_runbook(postmortem.service, postmortem.new_knowledge)
Pillar 3: Investment
Reliability work needs dedicated time:
Engineering time allocation:
Feature development: 60%
Reliability work: 20%
Tech debt reduction: 10%
Learning/experimentation: 10%
The 20% reliability budget includes:
- Alert tuning and noise reduction
- Runbook automation
- Chaos experiments
- SLO reviews and adjustments
- On-call process improvements
- Monitoring and observability improvements
Protect this 20%. When leadership pressures to ship more features, show the correlation between reliability investment and incident reduction.
Measuring Culture
You can't manage what you can't measure. Cultural metrics:
reliability_culture_metrics:
# Engineering engagement
postmortem_attendance_rate: target > 80%
action_item_completion_rate: target > 90%
runbook_update_frequency: target > 2x/month per service
# Design quality
design_docs_with_failure_modes: target > 95%
new_services_with_slos: target 100%
chaos_experiment_frequency: target > 1x/quarter per service
# Team health
oncall_nps: target > 0
developer_survey_reliability_confidence: target > 4/5
sre_team_attrition_rate: target < 10%/year
The Quick Wins
If you're starting from Level 1, here are the highest-impact changes:
- Week 1: Define SLOs for your top 3 services. Just availability + latency.
- Week 2: Make post-mortems mandatory and blameless. Use a template.
- Week 3: Set up on-call rotation with clear escalation paths.
- Week 4: Create a reliability Slack channel. Share learnings daily.
- Month 2: Start tracking error budgets. Share with product managers.
- Month 3: Run your first chaos experiment (kill a pod, see what happens).
Six weeks from chaos to competence. Not perfect, but dramatically better.
If you want to accelerate your reliability culture with AI-powered tools that embed SRE best practices, check out what we're building at Nova AI Ops.
Written by Dr. Samson Tanimawo
BSc · MSc · MBA · PhD
Founder & CEO, Nova AI Ops. https://novaaiops.com
