Operations | Monitoring | ITSM | DevOps | Cloud

Every AI Agent You Add Leaves Something Behind to Clean Up

Adding a second AI agent to a project feels like doubling your output. In practice, it usually means doubling your bookkeeping too. Every agent needs its own worktree so it can work without touching the branch someone else, human or otherwise, is using. Multiply that by five agents across three repos, and the isolation that made parallel work possible starts generating its own kind of work: which worktree goes with which branch, which ones are stale, which upstream nobody remembers creating.

Scheduled Autonomous AI SRE Agent as a Kubernetes Guardian: AURA

Some agent work should pause for a person. This is the other case: a health check every two minutes, one bounded action, and a result nobody approved. Each scheduled run starts the normal AURA image in one-shot mode: check one workload, act if something is wrong, write the result to the job log, and exit. Overlapping runs are forbidden.

AI can't correlate what was never standardized

Steve Flanders (Senior Director of Engineering, Splunk) makes the case that AI can't save an observability stack that never agreed on a standard. Mix formats across metrics and logs, and AI stops correlating and starts guessing, which means you either make the wrong call or miss the answer you actually needed. OpenTelemetry is one fix, but Prometheus and Fluentd work too. The standard matters more than which one you pick.

AI Model Drift: How to Keep Models Reliable

AI model drift is when an AI system's performance and accuracy degrades over time because the data, user behavior, or business environment has changed since the model was trained or evaluated. Even if latency, uptime, and infrastructure metrics remain healthy, model quality can quietly decline, leading to less accurate predictions, inconsistent responses, and reduced user trust.

Best AI Image Generators (2026)

If you still think that creating high-quality images must take a lot of time and resources, think again, because in 2026, that is no longer the case. Now, AI image generators can instantly help you turn simple ideas into impressive visuals in just a few seconds. It doesn't matter if you want to create content for social media, design more complex marketing materials, or just bring your most random ideas to life; there's probably an AI image generator that can help you achieve exactly what you need.

From Incident Data to Operational Knowledge: A Safer Role for Generative AI in IT Ops

IT operations teams produce an enormous amount of information. Alerts, logs, incident messages, deployment records, support tickets, runbooks and post-incident reviews all contain operational knowledge. The problem is that much of this knowledge remains fragmented and difficult to reuse. Generative artificial intelligence can help organise and transform this information, but its safest role is not unrestricted control over production infrastructure. Its strongest initial use cases involve reading, summarising, classifying and drafting information for an engineer to review.

Introducing the Flyway MCP Server: governed database change, now available to your AI coding assistant

AI coding assistants have changed how fast application code gets written. Copilot, Cursor, Claude Code, and agentic tools built on top of them can generate a working feature in minutes. But none of them know your database's history. They don't know that a migration already renamed that column last sprint, that a policy forbids unqualified DELETE statements, or that the target environment has drifted from what your migrations say it should look like.

What is AI ROI? Definition and why it matters

In 2025, 85% of organizations increased AI investment, and 91% plan to do the same this year, according to Deloitte. Despite continued spending, however, ROI lags behind, with just 6% seeing payback within one year. While AI use cases tend to have a longer payback period, often in the 2-4 year range, companies can’t afford to keep spending money without some measure of its practical impact both immediately and over time.

What are AI tokens? The unit your AI bill is written in

AI tokens are the small chunks of text, roughly four characters or three quarters of a word each, that language models read and generate. Every prompt and every response is measured in tokens, and AI providers bill per million of them. That makes the token the base unit of AI spend: 1,000 tokens is about 750 words, and every AI feature you ship is a token meter running.