Operations | Monitoring | ITSM | DevOps | Cloud

The OpenTelemetry (OTel) Collector plays a central role in collecting, processing, and exporting telemetry data. If you’re deploying the Collector in production, chances are you’ve reached for the otelcol-contrib distribution. It’s the easiest, most flexible, and most documented distribution, used in nearly every demo and getting-started guide. But here’s the catch: It’s not actually recommended for production use.

AIOps (Artificial Intelligence for IT Operations) is not only a game changer, but the need of the hour as modern IT grows and becomes increasingly complex. The promises of AIOps are both overwhelming and tantalizing. AI-powered monitoring and observability can help predict issues, automatically resolve incidents, and optimize performance across the IT infrastructure. However, onboarding an AIOps monitoring tool can be more complicated than it sounds on paper.

I’m pleased to announce the public beta of Honeycomb Hosted MCP, along with our first wave of one-click integrations for Cursor, Visual Studio Code, and Claude Desktop. We’re also very excited to announce that Hosted MCP is available on AWS AI Agents marketplace and for all Honeycomb plans (including our free plan!) at no charge. Honeycomb was built with a singular focus: how do we help teams become better at the art and craft of software development, delivery, and operations?

In growing organizations, network requests pile up and so does the risk of delays, miscommunication, or misconfigurations. Whether a developer needs a static IP for a staging server or a project team is scaling infrastructure, manual processes don’t scale with you. LightMesh Requestor Workflow gives your users a secure, controlled way to request static IP addresses — without granting access to your entire IP address management (IPAM) platform. Admins stay in control. IT team stays productive.

Agentic AI is making waves across various industries, and its potential to transform ITSM is undeniable. With all the talk about agentic AI capabilities, you are probably daydreaming about streamlining ITSM processes through automated incident management, automated change risk analysis, improved efficiency through service desk automation, or enhanced service quality with predictive maintenance. But you also may start to wonder: How real is all of this? Is my organization truly ready for agentic AI?

‍In this final part, the Scout team continues our talk with Freedom Dumalo, former CTO at Flexcar and current CTO at Vestmark. We discuss some essential questions about architecture, touch on Rails, Turbo, and Stimulus, and the key considerations for those starting off before they lock in an architecture or tech decision. ‍ By the way, before we jump in, Scout Error Monitoring is coming!

In 2020, we introduced the Snowflake Enterprise data source for Grafana, allowing users to seamlessly pull data from the Snowflake cloud-based data storage and analytics service into Grafana dashboards. Available for Grafana Enterprise and Grafana Cloud users, it’s a powerful way to not only query and visualize Snowlake data, but to do so alongside other data sources, so you can discover correlations and other meaningful insights within minutes.

There’s a certain comfort in the glow of your network operations center (NOC) dashboards. For some time, the sign of a well-run NOC was that sprawling bank of screens, each dedicated to a different domain. One for the WAN, showing link status. Another for the data center, tracking backbone health. A third for cloud consumption, pulling metrics from your provider. Each screen is a neatly bordered kingdom, diligently monitored by its own set of tools. As long as the lights are green, all is well.

As enterprises scale in complexity, the infrastructure landscape is no longer just cloud or on-premises, it’s both. Hybrid is the new normal and it’s here to stay. And with that shift comes a new demand: a unified, scalable observability solution that works across the entire tech stack, from legacy hardware to cloud-native workloads. That’s where LogicMonitor comes in.

Container image layers provide valuable insight into what goes into a container, including which packages were installed, what commands were run, and where vulnerabilities might live. The metadata associated with these image layers is essential for debugging, optimizing image size, and managing security risks. However, key container-layer metadata fields such as digest, size, and created_by are sometimes missing, which can disrupt important tasks.