#operations

Operating vector databases, embedding stores, and data layer infrastructure for Agentic AI platforms

Comprehensive governance documentation covering quality evaluation, operational playbooks, AI Gateway guardrails, compliance, and domain customization

AI platform monitoring, observability, evaluation, compliance, and domain-specific operations guide

Langfuse-based agent monitoring operations — monitoring architecture, key metrics, PromQL, alerting, and cost tracking (for tool comparison, see LLMOps Observability)

Agent execution tracing, LLM serving optimization monitoring, cache tuning quality validation, and agent lifecycle observability

AI Agent-based autonomous incident response — Strands/Kagent integration, Chaos Engineering + AI, ontology feedback loop

ML-based predictive scaling and anomaly detection — Karpenter+AI, CloudWatch Anomaly Detection, AI Right-Sizing

Security policy enforcement and operations tool performance benchmark

Best practices for stable EKS cluster operations including GitOps, troubleshooting, high availability, and Pod lifecycle management

Covers EKS Hybrid Nodes Mixed Mode operational patterns, Cluster Insights configuration validation, monitoring, and cost optimization based on vCPU-hour billing.

Operational best practices for EKS Hybrid Nodes — mixed mode workload placement, configuration validation with Cluster Insights and nodeadm debug, monitoring architecture, and cost optimization based on tiered vCPU-hour billing.

Kubernetes version upgrade strategy for EKS Hybrid Nodes — covers how nodeadm upgrade works, the mandatory manual cordon/drain, handling SSM signing key expiration (nodeadm 1.0.19+), private mirror configuration for air-gapped networks, and the upgrade runbook.