Operations | Monitoring | ITSM | DevOps | Cloud

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.

For decades, PHP has powered the web using a simple model: process a request, send a response, then shut down. This model, especially in the form of CGI and PHP-FPM, is easy to understand but increasingly inefficient for modern web demands.

Today, we’re announcing the general availability of Lumigo Copilot, the most intelligent AI-powered observability assistant on the market, built for the complexities of modern microservices. Copilot emerged from a simple realization: Distributed systems produce too much fragmented data across too many layers, making troubleshooting slow, reactive, and deeply manual. Copilot changes that.

Generative AI has moved beyond early-stage experiments into an emerging driver of enterprise value. By automating complex tasks, personalizing customer interactions at scale, and accelerating innovation cycles, organizations adopting Generative AI (GenAI) see measurable performance improvements. For businesses, the challenge now lies not merely in adoption but in precise alignment of AI capabilities to strategic business goals, driving revenue, optimizing costs, and mitigating risks effectively.

Your organization has a bold modernization roadmap. Cloud migration. Application updates. Enhanced customer experiences. New revenue streams. The business case is compelling, the stakeholders are aligned, and the budget is approved. Yet six months in, progress feels sluggish. The cloud migration is behind schedule due to performance issues no one anticipated. Application modernization stalled when the team discovered integration complexities that weren’t apparent during planning.

Grafana Tempo 2.8 is officially here, delivering new TraceQL features, performance improvements, and bug fixes, as well as some breaking changes. Watch the video below to learn more about the TraceQL features, or continue reading to get a quick overview of these and other updates. If you’re looking for something more in-depth for all of the changes that happened in this release, head over to the Grafana Tempo 2.8 release notes or the changelog.

It’s one thing to debug your game during development, but once your game is in production, visibility into errors and failures in real gameplay is harder to achieve. And when something does go wrong, players are more likely to ragequit than submit a bug ticket.