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.

Automation has always stirred up fears of job loss. For IT professionals, the conversation has only grown louder with the rise of AI. But the truth is that the future of IT is not about replacement—it’s about reinvention. For decades, IT has been defined by its firefighting: manually resolving tickets, managing endless alerts, and fielding repetitive service requests. These tasks are ripe for automation, but automation doesn’t eliminate the need for IT talent.

Everyone agrees security matters – yet when it comes to big data analytics with Apache Spark, it’s not just another checkbox. Spark’s open source Java architecture introduces special security concerns that, if neglected, can quietly reveal sensitive information and interrupt vital functions.

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.

This post, part six of our OWASP CI/CD Top 10 series, looks at some of the common risks associated with Insufficient Credential Hygiene. By better understanding the flaws that affect credential hygiene, we can better understand how even the most sophisticated pipelines were compromised.

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.