Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on APIs, Mobile, AI, Machine Learning, IoT, Open Source and more!

Instant Kubernetes Observability with Proxymock #speedscale #kubernetes #ebpf #devops #cloudnative

Learn how to get instant observability into your Kubernetes cluster by installing the Speedscale operator and proxymock tool. In this step-by-step tutorial, we walk you through setting up the operator to capture live network traffic (including encrypted traffic, API calls, and database calls) without complex instrumentation or manual configuration.

SDLC Phases and the Reliability Gap AI Can't Close

Decisions in each SDLC phase from planning to design, development, testing, deployment, and maintenance are made without sight of live production behavior. AI coding agents are widening that visibility gap faster, working faster than human engineers ever could. This piece maps exactly how this gap presents at each phase, and the harm that this brings.

Introducing Harness Agent DLC: Extending your SDLC to AI Agents

Harness Agent DLC: Ship AI Agents to Production Safely Building an AI agent is easy. Getting one into production safely is where teams get stuck. Harness Agent DLC extends the software delivery lifecycle to AI agents, giving teams a clear path to evaluate, deploy, secure, observe, and optimize agents in production. Learn more: Because agents dynamically choose their own tools, APIs, and actions, their behavior can change every time they run. Harness Agent DLC gives engineering teams the controls needed to move beyond experiments and operate agents safely at scale.

Optical Freedom in the Age of AI: Why Thin Transponders Are Reshaping Optical Network Design

AI is driving the next wave of digital transformation, but it is also creating an unexpected challenge for network operators: optical capacity is becoming a strategic bottleneck. The same AI boom fueling billions of dollars in data center investment is placing unprecedented demand on optical networking infrastructure.

MCP for SLA Monitoring: Uptime, MTTR & MTTA

MCP for SLA monitoring gives an AI agent direct access to measured uptime, mean time to resolve (MTTR), mean time to acknowledge (MTTA), outages, and reliability risks. With Hyperping, you can ask Claude, Cursor, Codex, or another MCP client for an SLA report and get an answer based on your live monitoring data instead of copying numbers between dashboards. The distinction between monitoring data and SLA compliance matters. Hyperping measures availability and incident response.

Introducing Harness AgentTrace: An Observability and Guardrail Framework for AI Agents | Harness Blog

AI agents fail differently from the software we spent the last two decades learning to monitor. We hear some version of the same story from teams shipping agents to production: an agent starts producing wrong answers. Not obviously broken: confident, well-formatted, plausible wrong. The logs are clean, latency looks healthy, and error rates sit at zero. Nothing flags a problem. A user eventually does.

Introducing AI Agent Deployment in Harness Continuous Delivery | Harness Blog

‍Teams building agents have converged on something that looks a lot like the software development lifecycle, but reshaped around a system whose output isn't deterministic: prototype an agent against a framework, evaluate it against a dataset of expected behavior, deploy it somewhere real, observe how it behaves against live traffic, and feed what you learn back into the next prototype. Call it the agent development lifecycle (Agent DLC).