Operations | Monitoring | ITSM | DevOps | Cloud

Last year, at the annual conference for an open source framework you've definitely heard of, I walked up to the founder in a room outside the main stage. He was hunched over his laptop, frantic. We've known each other for a few years. "What's going on? Is everything okay?" He looked up with the specific shade of white people only get when they realize they've made a big mistake.

Context engineering is the practice of managing the information an AI model sees (documents, tool outputs, memory, and structured metadata about the systems it reasons over) so it can make accurate decisions inside a real engineering organization. Most engineering teams have access to the same AI coding agents: Claude, GPT, Gemini, the major variants everyone is shipping. The model is no longer the differentiator.

I recently gave a talk at Experts Live India 2026 about SquaredUp, and even before getting into the demo, there was one question I knew I had to address: Is the dashboard era over? It's something we're all hearing more. "Just ask AI." "Agentic AI will build your dashboards automatically." "Why bother with static views when a chatbot can answer anything?" It's a fair question. Answering it requires a clear understanding of what a dashboard represents.

Quick Answer A Network Operations Center (NOC) — pronounced “knock” — is a centralized physical or virtual facility where IT professionals monitor, manage, and maintain an organization’s network infrastructure on a 24/7/365 basis. The NOC serves as the nerve center for detecting incidents, coordinating responses, and ensuring maximum network availability and performance.

When an unexpected alert fires these days, most engineers' first move is to ask their AI assistant for help.You ask why your checkout service is slow and the assistant gets to work, but it can't get any meaningful insights—at least not quickly—without the proper guidance. So, the next thing you know you're sharing deals about your existing data sources, the services you have running, how they connect, which labels and metrics matter, and on and on.

You’re comparing error monitoring tools. You’ve narrowed it down to two or three options. Now you need to know what this actually costs before you bring it to your team. Here’s what Rollbar costs, what’s included at each tier, and how it compares to Sentry and Datadog on pricing. No sales pitch, just the math.

InfluxDB 3 Explorer 1.8 is all about writing data and keeping it under control. You can now subscribe to MQTT, Kafka, and AMQP streams directly from Explorer, generate custom sample datasets, stream live sample data continuously into your database, and validate your line protocol and preview the resulting schema before you write it. You can now also view and edit retention periods on both databases and individual tables.

An alert in the middle of the night warns of a potential business failure. Manual incident response becomes more complex due to the overwhelming data from distributed and dynamic digital services. With an SRE agent, your engineering team can cut through alert clutter. They can sort through various signals quicker, decreasing burnout and achieving faster, more affordable resolutions. Operational resilience will see its next evolution with Agentic AI.

Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.

Modern enterprise architectures increasingly need to bridge the gap between resource-constrained IoT devices and heavyweight enterprise backend systems. ActiveMQ MQTT support makes this possible: devices running the MQTT protocol - sensors, actuators, edge nodes, publish telemetry on standard topics, while JMS-based backend services consume and process the data without any client-code changes.