Operations | Monitoring | ITSM | DevOps | Cloud

You shipped your AI app. But do you have all the answers? Do you actually know which model ran, how many tokens it consumed, or why it stopped? This is what LLM observability gives you, and most AI engineers are skipping it entirely. I built an SOS detection app and used OpenTelemetry to get full visibility into every single call. Token usage, model version, finish reason, and cost per call all in one place, standardised across any provider. Check out the OpenTelemetry GenAI docs in the link below; there is a lot more you can track than you think.

Learn how to implement end-to-end monitoring and observability for Anthropic (Claude) API-based applications using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Anthropic API calls, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability, so you can debug faster, optimize latency, and confidently run Claude-powered AI systems at scale.

About This Video: If you're working with OpenTelemetry, managing collector configurations across environments like VMs can quickly become difficult. In this video, we focus on VM-based setups and walk through how to configure SigNoz Collection Agents step by step. We start with an introduction to VM collection agents, then move into a practical project walkthrough using the OpenTelemetry demo. From there, we explore the documentation, set up configurations, run the collector, and finally validate everything inside SigNoz.

Learn how to monitor your n8n Cloud workflow executions using OpenTelemetry by capturing traces and sending them directly to SigNoz for real-time visibility into performance, errors, and execution flow.

Learn how to monitor your n8n Cloud workflow executions using OpenTelemetry by capturing traces and sending them directly to SigNoz for real-time visibility into performance, errors, and execution flow.

Learn how to implement end-to-end monitoring and observability for Agno-based AI systems using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Agno workflows, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability—so you can debug faster, optimize latency, and confidently run AI systems at scale.

Learn how to implement monitoring and observability for OpenClaw systems using OpenTelemetry and SigNoz. In this video, we cover how to instrument OpenClaw, collect traces, metrics, and logs, and visualize everything in SigNoz for real-time insights into performance and reliability. You’ll see how to quickly identify bottlenecks, debug issues, and improve system stability in production.

Learn how to implement monitoring and observability for the Claude Agent SDK using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Claude-based agents, capturing traces, metrics, and logs, and visualizing everything in SigNoz for real-time insights. You’ll learn how to debug agent behavior, identify latency bottlenecks, and monitor performance in production environments.

SigNoz uses distributed tracing to gain visibility into your software stack. If you need any clarification or find something missing, feel free to raise a GitHub issue with the label documentation or reach out to us at the community slack channel.

AI agents are powerful, but debugging them in production is hard. Non-deterministic behavior, LLM latency, and token costs create observability challenges that traditional monitoring tools don't address. In this webinar, engineers from Inkeep and SigNoz walk through how Inkeep monitors its AI agent framework in production using OpenTelemetry-native observability.