Operations | Monitoring | ITSM | DevOps | Cloud

As AI workloads and cloud-native applications expand, organizations are generating more log data than ever. Each service, container, and model inference produces continuous telemetry that must be stored, secured, and analyzed. As telemetry grows more complex, teams must balance full visibility with new retention and residency needs.

In fast-moving organizations, developers are expected to ship quickly. Infrastructure shouldn’t be a blocker, but it can’t become a liability either. One unchecked terraform apply, a missing tag, or a misconfigured instance can turn into a surprise bill, a failed audit, or even a production outage. The most reliable way to manage infrastructure at speed is to make governance part of the delivery process.

GCP Cloud Monitoring probe let you transform your existing GCP metrics into automated pass/fail validation for chaos experiments, eliminating subjective observation in favor of objective measurement. With flexible authentication options (workload identity or service account keys) and PromQL query support, you can validate infrastructure performance against defined thresholds during controlled failure scenarios.

Harness now supports the Model Context Protocol (MCP) for Feature Management and Experimentation (FME), enabling developers to interact with feature flags directly from AI-powered IDEs like Claude Code and Windsurf. The FME MCP tools make it easier to explore, understand, and manage feature flags through natural language, streamlining delivery and release workflows without leaving your development environment.

Your users are complaining about slow application performance. Your monitoring dashboard shows all devices are green, routers operational, switches functioning, and bandwidth utilization is normal. Yet something is clearly wrong. The problem isn't your equipment; it's the path between your users and their destinations. This is where network path monitoring comes in.

What approaches exist, and how to use automation to benefit the overall business processes.

Across the IT and observability landscape, enterprise leaders are facing a troubling pattern. A trusted vendor announces a “modernization initiative,” often following a major acquisition or a shift in ownership. Overnight, pricing structures change, license models disappear, and long-time customers are pressured into multi-year bundles under the banner of innovation. What’s being framed as progress often feels more like pressure.

Testing ecosystems contain massive amounts of data, including outlined test scenarios, prerequisite configurations, and the tests themselves. As a result, these ecosystems are prone to data sprawl. This makes it difficult to prevent configuration drift and quickly spin up new tests, especially at the frequency needed to support a fast-growing application. Teams can handle these challenges by treating their tests as part of their application infrastructure.

As AI workloads and cloud-native applications expand, organizations are generating more log data than ever. Each service, container, and model inference produces continuous telemetry that must be stored, secured, and analyzed. As telemetry grows more complex, teams must balance full visibility with new retention and residency needs.

Imagine you have an API service composed of multiple microservices. Traffic fluctuates — sometimes light, sometimes spiking. Without Fargate, you’d have to manage EC2 instances, autoscaling, patching, and more. With Fargate, you define each microservice as a task, setting the CPU/memory, container image, network rules, and AWS schedules, and then run them as needed. The result: faster deployment, lower ops overhead, and smooth scaling.