Spotlight

Your Observability Is Looking at the Wrong Things

Benard Otieno

This article argues that uptime, latency and error rate only tell you a system is alive, not correct, and shows which behaviour-level signals and baselines belong in dashboards and alerts instead.

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Tools and utilities

  • kubectl-netshoot: network troubleshooting plugin for kubectl

    kubectl-netshoot is a kubectl plugin that spins up a netshoot container in a pod or on a node, so you can debug cluster networking without adding any tools to your own images.

  • G-Kubewall

    kubewall provides a simple and rich real time interface to manage and investigate your clusters.

  • Hortator

    Hortator lets AI agents spawn sub-agents at runtime, with each agent running in its own pod with budget caps, network policies, PII redaction, and capability inheritance so children can never escalate beyond their parent's permissions.

  • High-Performance NVMe-oF Storage for Kubernetes

    If you're running I/O-intensive workloads, traditional block storage latency becomes a bottleneck.

  • SnowOps Labs: Hands-On Kubernetes Failure Lab

    SnowOps Labs is a Kubernetes practice lab that builds a local cluster, injects realistic failures, and grades your fixes through guided scenarios and incidents.

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Events starting soon

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Building Platforms for AI Agents
Building Platforms for AI Agents

Non-deterministic agents pose specific challenges for platform teams in observability, state management, governance, and trust.

Mauricio (Salaboy) Salatino explains why agentic applications behave like distributed multi-agent systems. His test assigned agents to take an order, cook the pizza, deliver it, and charge the customer. One order crossed 15 containers and produced 200 traces.

In this interview:

  • Why agent frameworks can recreate monolith scaling and resource contention
  • How OpenTelemetry data can measure agent behavior and trust over time
  • Why narrowly scoped agents are safer than agents that follow long sequences
  • How the platform can become the learning layer that feeds better context back to LLMs

Learn from production

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Matching jobs

    • DevOps Engineer with 42dot

    • Salary: $145K to $240K a year

    • Location: based in the office in Sunnyvale, CA, USA

    • Tech stack: Kubernetes, AWS, Helm, Go, Python, CDK, Cloudformation, Pulumi, Terraform, Gitlab

    • DevOps Engineer with RapidFort, Inc.

    • Salary: $125.82K to $308K a year

    • Location: remote from

    • Tech stack: Kubernetes, AWS, Azure, GCP, Helm, Python, Terraform, Datadog, Grafana, Prometheus

    • DevOps Engineer with T-Systems ICT India Pvt. Ltd.

    • Salary: $54K to $4.13L a year

    • Location: based in the office in Pune, IN

    • Tech stack: Kubernetes, Helm, Docker, C#, Typescript, Terraform, GitHub Actions, Grafana, Prometheus, Grafana Tempo

    • DevSecOps Engineer with Coupang

    • Salary: $136.8K to $365.64K a year

    • Location: based in the office in Seoul, KR

    • Tech stack: Kubernetes, AWS, Azure, GCP, Docker, Python, Shell

    • DevSecOps Engineer with Higgsfield

    • Salary: $70.74K to $440K a year

    • Location: based in the office (and remote from home) in San Francisco, CA, USA

    • Tech stack: Kubernetes, Go, Python

Discover more Kubernetes jobs on Kube Careers →

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