Operations | Monitoring | ITSM | DevOps | Cloud

In most object-oriented languages, exceptions are an extremely powerful mechanism for dealing with unexpected situations that arise when running your code. PHP has supported robust exception handling since PHP 7.0. As you begin your programming journey, exceptions are a source of tremendous pain. Over time, you grow to appreciate the value they bring.

AWS gives you the power to scale, deploy, and innovate at speed. However, with that speed comes a good amount of complexity. Services multiply, resources balloon, and performance issues sneak in when you least expect them. That’s where monitoring comes in. But it isn’t about checking boxes on dashboards. It’s about getting the most value for every dollar you spend or, maximizing your return on investment (ROI) from AWS monitoring. So, how do you actually do that?

The HaloPSA plugin is a new addition to SquaredUp, and helps you create live dashboards that surface the important metrics – giving you and your team a single pane of glass for help desk performance, asset visibility, and client reporting. Why it matters: If your team uses HaloPSA to manage tickets, assets, and clients, then you already know how vital that data is for running smooth operations.

There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.” But these aren’t checkpoints on a single journey to automation. DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.

You’ve probably felt the pain: data pouring in from every corner of your stack, tools choking on volume, dashboards lagging behind reality, alerts firing (or worse, not firing) without context. If that sounds familiar, it’s time to get serious about telemetry pipelines. Whether you're an SRE trying to stabilize a flapping service or a developer navigating multi-cloud chaos, a telemetry pipeline helps you take control of the data firehose.

We are exploring how we can help on-call engineers troubleshoot incidents more effectively by providing the OpenAI Codex agent with access to real-time observability data in terminals. We've developed an integration and new tool visualizations that connect OpenAI's Codex CLI to the new Datadog MCP server. In this post, we'll share what we've been experimenting with: enabling an AI agent to retrieve production metrics, logs, and incidents from Datadog in real time and act on that context.