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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

AI can write database code fast. Here's how to keep it safe before production.

AI can write database schema changes in seconds, but nothing should reach production until it's validated, tested, and approved. In this discussion, Ken Muse (GitHub), Steve Jones (Redgate), and Huxley Kendall (Redgate) show how a governed pipeline keeps AI-generated database changes safe without slowing teams down.

Resilience Testing Agents: Find Resilience Risk Before It Reaches Production

Most engineering orgs know that resilience testing matters, but proving ROI before you invest time and money is hard. Harness RT Agents solve that by scanning your CD pipelines for resilience risk first, no instrumentation needed, so you get a real report before you commit to chaos experiments, load tests, or DR testing. In this video: Resilience Testing is free to start, with a full fault library, a hosted control plane, and RBAC included.

The gap between individual AI productivity and team performance

As a product manager at Upsun with a computer engineering background, Kateryna Dvornichenko had spent months researching competing tools in the agentic development space, running tests, comparing features, and building a picture of where the market was heading. She realized the tools were impressive, but something kept standing out. "Collaboration was not the strong point of any of them," she says. "Everyone stays on their own machine with their own setup.".

Repo rightsizing: audit every model call in a repo you already shipped

Repo rightsizing is a single-pass audit of every real model call in a codebase you already shipped: SDK invocations, sub-agent dispatch sites, and agent frontmatter pins. Each call site is scored on the job it actually does, and the result commits as one blueprint file you can diff next quarter. It replaces one-skill-at-a-time reviews, which miss files where a single model key covers two different jobs.

AI usage tracking: Monitor spend by team, feature & model

AI usage tracking means measuring who and what consumes AI across your company, by team, feature, and model, then converting the usage into spend and cost per unit of work. Provider consoles stop at totals per API key. Tracking puts names on those totals: which team, which product, which model, and whether any of it was worth the money. In May 2026, CNBC reported that “almost every Fortune 500 is tracking overall AI usage,” quoting ModelOp CTO Jim Olsen. The same reporting carried his warning.

Shipped: Find the S3 buckets paying early delete fees

S3 lifecycle rules move data to Standard-IA or Glacier to cut storage cost. CloudZero now flags the buckets where that move backfires: an early delete fee is charged when an object leaves its tier before the tier’s minimum storage duration. The cause isn’t always a misconfigured lifecycle rule. A manual delete, an overwrite, or an object written straight into the tier by a replication or backup job produce the identical charge.

Major Incident Management: A Playbook for I&O Teams

It's 2 a.m. Monitoring alerts are firing, the on-call engineer is being paged, and customer reports are arriving faster than anyone can triage them. A bridge call opens. Infrastructure, network, application, and service desk teams hop on with different fragments of context. Meanwhile, executives want to know the scope, customer impact, and expected recovery time. This is not the moment to decide who is in command or how often updates should go out.