Operations | Monitoring | ITSM | DevOps | Cloud

A stage-by-stage playbook for automating incident response: what to automate at detection, triage, and remediation, what to deliberately leave manual, and a checklist to run against your current setup. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

Enterprise AI agent programs overspend because leaders optimize token costs, which represent just 20-25% of variable run costs. Human oversight accounts for 70-75%. The real fix is reducing exception rates and embedding governance directly into agent execution from the start.

A frequently encountered issue when exploring database optimizations is proving that a proposed query rewrite is actually faster than the existing query. Often, proving this isn’t just as simple as running both queries and comparing their execution times. When your hot queries are measured in milliseconds, factors such as cache states, concurrent queries competing for CPU and I/O, and noisy neighbors can mask the true performance difference between query versions.

Developers and SREs who use Bitbucket Cloud often need to move between their source code, observability data, test results, and security findings to investigate a problem. Connecting an error or slow method to the correct repository, file, and commit takes time. Teams may also discover vulnerabilities, infrastructure misconfigurations, coverage regressions, or flaky tests only after code has progressed further through the software development life cycle, when remediation is more disruptive.

NestJS 12 dropped last week with ESM packages, Standard Schema validation, a rebuilt CLI, and a brand-new observability SDK. That last one is the most interesting part for anyone running NestJS in production with an APM tool. At Scout Monitoring, we instrument NestJS applications through our Node.js agent. A framework shipping its own observability layer changes the conversation for every APM vendor in the ecosystem, including us. Here is what you need to know.

An application slows down every month-end. You open Hyper-V Manager, the virtual machine looks healthy, and the ticket closes without a cause. Next month it happens again. Native consoles show live state and keep no history behind it, so nobody can prove whether the fault was the guest, the host, or a storage path shared with nine other workloads. That gap sets what a Hyper-V deployment costs to run, measured in unplanned downtime and in engineering hours spent guessing. This guide covers.