Operations | Monitoring | ITSM | DevOps | Cloud

As development teams adopt AI-powered tools and build services that make use of AI agents, they want to extend their AI capabilities to incorporate familiar tools and observability data. However, AI agents struggle with regular API endpoints and frequently fail when parsing complex nested JSON hierarchies or incorrectly handling errors. As a result, these agents often fail to retrieve relevant results.

As organizations bring more AI and LLM workloads into production, the underlying GPU infrastructure that supports these workloads becomes even more critical in ensuring these workloads remain fast, reliable, and scalable. Inefficient GPU resource usage, for instance, can lead to longer runtimes and reduced throughput, negatively impacting overall model performance. Additionally, idle and underutilized GPUs can quickly drive up costs and lead to needless spending.

Your Kafka producer pushed a million messages yesterday. Nice. But can you tell if they all made it? Or why did latency spike at 2 PM? Producer metrics help you determine that. They expose how long messages take to send, whether messages are getting stuck, and whether retries are piling up. Let’s go over which ones help while debugging and how to monitor them.

Grafana Play is a free and publicly accessible sandbox environment that allows users to explore and learn Grafana without setting up their own instance. Grafana Play comes preloaded with ready-made sample dashboards, and showcases how to work with different data sources, create visualizations, and use advanced Grafana features.

Unless you’ve been living under a rock, “MCP” is probably a term you’ve heard thrown around in the AI space. Each of the editors and LLM providers have been racing to add and enhance their MCP support. Sentry was fortunate enough to be included in Anthropics release announcements for MCP.

In an era where hybrid environments and AI-driven innovations redefine enterprise operations, organizations face increasing complexity, disruption, and vulnerability in their systems. To overcome this growing challenge, Cisco and Splunk are working together to harness the power of AI to help customers ensure that digital resilience is an inherent part of their systems.

OpenTelemetry is emerging as the common framework for collecting observability data, and for good reason. It’s vendor-neutral, open source, and designed to collect traces, metrics, and logs in a consistent way. But while most of the buzz is around tracing and metrics, let’s not forget: logs are still the backbone of investigation and response. That’s why Graylog now supports native collection of OpenTelemetry data over gRPC.

When you hear “spill the tea,” you probably think of pop culture, not outages or anomalies. But the origin may surprise you: before it was slang for juicy gossip, ‘tea’ was actually ‘T,’ which represents truth. We know what you’re thinking: “Are you trying to say ‘spilling the tea’ is a good thing?” And yes, that’s exactly what we’re saying, especially when your logs are doing the talking.

As businesses scale, so do their observability needs, but many find themselves stuck with costly, inflexible platforms that no longer serve them. Despite mounting frustrations, the complexity of migration keeps companies from making a change. The risk of losing critical data, disrupting workflows, or rebuilding everything from scratch often outweighs the benefits of switching. Most vendors offer little to no migration support, forcing teams to manually reconfigure dashboards, alerts, and integrations.