Postmortems are where organizations learn from incidents — AI assistance makes postmortem drafting faster and more thorough, but the human insight that drives real change still requires engineering judgment.
Runbook automation with AI agents can handle routine remediation steps autonomously, but the quality of your runbooks determines the quality of your automation — here's how to build it safely.
An enterprise AI platform isn't one system — it's six components (gateway, model registry, vector infra, eval harness, observability, cost control) that need to work together without any one team owning the whole stack.
Log volumes grow faster than on-call headcount — AI-powered log analysis helps engineers find the logs that matter without reading millions of lines, but the implementation details determine whether it works.
Alert fatigue is an operations crisis — AI-driven correlation and anomaly detection can genuinely reduce noise, but only if you invest in the data quality and tuning that makes it work.
AIOps has been overpromised for years — here's an honest assessment of which AI applications in operations genuinely reduce toil in 2026, and which are still looking for a problem to solve.
Most organizations are overconfident about their AI engineering maturity — here's a five-level model with real indicators that show where you actually are and what it takes to advance.
AI engineering is fragmenting into distinct specializations with different skill sets and career trajectories — here's how to navigate the emerging landscape as an engineer who builds or works with AI systems.
When AI wrote significant portions of the codebase, traditional onboarding breaks — new engineers face code that's locally coherent but globally opaque, without the commit history and PR conversations that carry intent.
Velocity and DORA metrics don't capture what changes when a team adopts AI coding tools — here's what to measure instead, and what the real productivity story looks like at 6 and 12 months.