Kubernetes 1.36’s pod-level resource managers reshape more than scheduling—they redefine observability signals. Here’s how memory QoS and pod-scoped controls impact AIOps baselines, forecasting, and automation.
Terraform shows green. Controllers report success. Production still fails. This analysis reframes AIOps as a truth-detection layer above declarative systems.
A rigorous blueprint for calibrating trust in AI agents across CI/CD and production workflows. Learn how to combine confidence scoring, guardrails, human review, and progressive autonomy.
AI is accelerating DevOps delivery—but at what cost? Explore how velocity, error budgets, and AIOps must align to prevent systemic fragility and SLO debt.
AI agents are entering production pipelines, but autonomy without governance creates systemic risk. Explore a calibrated trust model and architectural patterns for safe AIOps adoption.
Kubernetes 1.36 tightens staleness handling and kubelet authorization. Here’s what those changes mean for AIOps signal quality and production observability.
Learn how to build a runbook-aware AI incident investigator on Kubernetes using events, OpenTelemetry, and structured guardrails for safe, transparent diagnostics.
A practical framework for running AI agents in production IT Ops. Learn how to define agent SLOs, implement guardrails, model failure modes, and design safe rollback strategies.
A practitioner’s blueprint for operationalizing continuous profiling in AIOps. Learn how to connect profiles with metrics, traces, and ML for automated performance optimization.
Learn how to integrate continuous profiling into your AIOps pipeline. Correlate profiles with incidents, reduce noisy workloads, and accelerate root cause analysis in production.
Build an end-to-end AI-powered Kubernetes investigation workflow using OpenTelemetry, structured runbooks, and LLM reasoning—complete with prompts and evaluation guidance.