Operations | Monitoring | ITSM | DevOps | Cloud

As the size and complexity of their relational database workload grows, every company eventually goes through the process of off-loading work on a read replica. It comes with lots of benefits, but at a cost of increased complexity. This article is about how we dealt with that, a lot of learnings, and some useful techniques. incident.io is an incident management product relied on by thousands of customers to be the thing that supports them through anything from a minor blip to a full outage.

Utility organizations preparing for CIP-015 should think beyond compliance. The organizations that will be best positioned are those investing in comprehensive network visibility, continuous operational intelligence, and segmentation to strengthen cyber resilience and operational continuity. This article explains what CIP-015 means, why it matters, and how Teneo helps utilities build a practical roadmap toward operational resilience.

‍ Here is a story platform engineering teams know by heart: developers find a shiny new tool, start building at a breakneck pace, and before you know it, the organization is drowning in a massive wave of unmanaged components. Right now, that exact story is playing out with generative AI.

How to find, search and retain the SharePoint audit log in Microsoft 365 - the Purview audit search, Search-UnifiedAuditLog PowerShell, retention limits, and where the native audit trail falls short.

‍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).

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.

Enterprise workflow automation can deliver faster resolution and ultimately more time back for IT teams. But not every workflow is ready to automate on day one, is it? This is where many automation programs stumble. Teams often start with signals like the most visible problem or the workflow that creates the most frustration. These are useful signals, but they’re not enough on their own. The best automation candidates are usually repeatable, measurable, and have clear inputs and outputs.

“AI-powered” has become a marketing label applied to almost any data center software feature. A more useful and precise term is actionable intelligence — a four-level maturity model (descriptive, diagnostic, predictive/prescriptive, and cognitive) that shows whether a platform’s AI claims are backed by real data infrastructure or just a chatbot layered on top of an incomplete system.