AI Agent Monitoring and Operations
Langfuse-based agent monitoring operations — monitoring architecture, key metrics, PromQL, alerting, and cost tracking (for tool comparison, see LLMOps Observability)
Langfuse-based agent monitoring operations — monitoring architecture, key metrics, PromQL, alerting, and cost tracking (for tool comparison, see LLMOps Observability)
AI Agent-based autonomous incident response — Strands/Kagent integration, Chaos Engineering + AI, ontology feedback loop
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
Agent execution tracing, LLM serving optimization monitoring, cache tuning quality validation, and agent lifecycle observability
Covers EKS Hybrid Nodes Mixed Mode operational patterns, Cluster Insights configuration validation, monitoring, and cost optimization based on vCPU-hour billing.
AI platform monitoring, observability, evaluation, compliance, and domain-specific operations guide
Best practices for stable EKS cluster operations including GitOps, troubleshooting, high availability, and Pod lifecycle management
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.
ML-based predictive scaling and anomaly detection — Karpenter+AI, CloudWatch Anomaly Detection, AI Right-Sizing
Security policy enforcement and operations tool performance benchmark
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.