Comprehensive guides for building reliable systems and effective incident response processes.
77 articles, frameworks, and resources
Build a world-class incident management process. Learn frameworks for detection, response, communication, and learning from incidents to build more reliable systems.
Step-by-step incident response playbook for database outages with clear actions, diagnosis steps, and post-incident procedures.
Pre-configured Grafana dashboard for tracking the four key DORA metrics: deployment frequency, lead time, MTTR, and change failure rate.
Measure how quickly your team restores service after an incident. A key DORA metric that indicates your organization's resilience.
Track the percentage of failed requests. Critical for reliability, user experience, and incident detection.
Track system availability and uptime percentage. Essential for SLAs, reliability, and customer trust.
Track the percentage of deployments that result in failures, rollbacks, or hotfixes. Essential for balancing speed with stability.
A structured template for blameless incident analysis with timeline, root cause, and action items.
A battle-tested framework for handling production incidents—from the first alert to the blameless post-mortem. Includes severity classification, escalation playbooks, communication templates, and lessons from real outages.
Map the evolution of observability tooling from custom scripts to SaaS platforms. Understand when to build, when to buy, and how to avoid the commodity trap.
AI is moving from experimentation to operational embedding (SRE, analytics, and autonomous agents), and the limiting factor is shifting from scale to trust: governance, security, auditability, and...
Engineering organizations are turning AI into an enforcement layer: standards, security, and reliability controls are being embedded directly into pipelines and runtime systems, rather than living as...
AI agent adoption is shifting from demos to operational systems, forcing CTOs to treat agents as production software with SRE-grade telemetry, security controls, and cost-aware model routing.
AI programs are shifting from single-model adoption to “AI operations” as a discipline: routing across multiple models, tightening safety controls, and rebuilding observability plus infrastructure...
Agentic AI is moving from stateless prompts to long-running workflows with persistent compute, tool execution, and enterprise governance.
RabbitMQ consulting: how to pick the right help and get real outcomes
Engineering orgs are adopting model-driven automation (graphs, state machines, continuous behavioral analysis) to keep reliability and security intact as AI-assisted and agentic development...
Engineering organizations are standardizing on agentic systems that execute multi-step work (incident investigation, code migrations, performance changes, data context building), which is forcing new...
Agentic, AI-assisted cyberattacks are pushing security programs toward radical transparency, faster detection-to-response cycles, and renewed focus on supply-chain and “boring” dependency risk...
AI-era infrastructure is moving from “scale compute” to “govern compute”: energy limits, cost controls, and reliability requirements are converging into a single operating model that spans data...
Engineering orgs are consolidating fragmented ingestion, processing, and ML training workflows into shared internal platforms, then adding governance and measurement (reliability, cost, carbon) as...
Engineering teams are rebuilding content ingestion and processing into governed, observable platforms to support AI at scale, because reliability, security, and regulatory scrutiny now sit on the...
Have experience to share? We welcome contributions from technical leaders.
Learn how to contribute