Operations | Monitoring | ITSM | DevOps | Cloud

Today at Google Next, Charity Majors demonstrated how to use Honeycomb to find unexpected problems in our generative AI integration. Software components that integrate with AI products like Google’s Gemini are powerful in their ability to surprise us. Nondeterministic behavior means there is no such thing as “fully tested.” Never has there been more of a need for testing in production!

There’s more pressure than ever to deliver high-availability Kubernetes systems, but there’s a combination of organizational and technological hurdles that make this ‌easier said than done. Technologically, Kubernetes is complex and ephemeral, with deployments that span infrastructure, cluster, node, and pod layers. And like with any complex and ephemeral system, the large amount of constantly-changing parts opens the possibility for sudden, unexpected failures.

Since the release of ChatGPT, there’s been growing excitement about the potential of generative AI—a class of artificial intelligence trained on pre-existing datasets to generate text, images, videos, and other media—to transform global businesses. Last year, we released our own generative AI-powered DevOps copilot called Bits AI in private beta. Bits AI provides a conversational UI to explore observability data using natural language.

Monitoring SNMP (Simple Network Management Protocol) devices is crucial for maintaining network health and security, enabling early detection of issues and proactive troubleshooting. Continuous monitoring ensures efficient resource utilization, minimizes downtime, and enhances overall network performance. In this article, we'll detail how to use the Telegraf agent to collect SNMP (MIB) performance statistics that you can forward to a data source.