Operations | Monitoring | ITSM | DevOps | Cloud

Learn how to implement end to end monitoring and observability for GitHub Copilot Chat using OpenTelemetry and SigNoz. In this video, we walk through enabling the OpenTelemetry exporter built into the Copilot Chat extension in VS Code, collecting a trace for every agent turn, and visualizing everything in SigNoz to gain real time visibility into model calls, tool executions, token usage, prompt cache savings, latency, and failures. Copilot Chat ships its own OTLP exporter, so there is no instrumentation library to install and no collector to run.

Learn how to implement end to end observability and monitoring for Grok Build, xAI's terminal coding agent, using OpenTelemetry and SigNoz. In this video, we walk through turning on Grok Build's native OpenTelemetry exporter, collecting metrics and structured session events, and visualizing everything in SigNoz to gain real time visibility into token usage, sessions and turns, tool calls and their outcomes, error categories, and startup latency. Grok Build ships its own exporter, so instrumenting it is a matter of configuration, with no library to install and no collector to run.

Agent Tracing shows the full execution path of an AI agent: the model call, every tool invocation and its arguments, token counts, cost, and the span where it broke. Same traces and spans you already use, with agent-specific attributes on top. Serge walks through three apps — a Next.js e-commerce agent using the AI SDK with a failing tool call, a Slack bot built with Eve that orders lunch, and a code review agent built with Flue over MCP.

A quick walkthrough of SigNoz Cloud's new trace detail view, with a flame graph that renders 100,000 spans in a single load. AI and agent workloads are producing traces with much higher span counts. We rebuilt the trace detail view in SigNoz Cloud to make investigating large traces faster. Here's what's new.

A quick walkthrough of SigNoz Cloud's new dashboard schema, redesigned to make dashboard operations by AI agents faster, more reliable, and lighter on tokens. AI agents are increasingly creating and editing observability dashboards. We redesigned the SigNoz Cloud dashboard data model with a structured, strictly validated schema so agents can work against defined fields and paths instead of inferring the dashboard structure.

We will look at some new features: trace diff and span pruning Can't comment in the chat? You may need to create a channel. Join us live for an introduction to flame graphs. We’ll cover what they are, how to read them, and how to use them to find performance bottlenecks in your applications. Bring your questions! Grafana Cloud is the easiest way to get started with Grafana dashboards, metrics, logs, traces, and profiles. Our forever-free tier includes access to 10k metrics, 50GB logs, 50GB traces and more.

OpenTelemetry instrumentation used to mean hours of manual work, wiring up metrics service by service. Now you can just ask for it. Tell an AI agent what you're trying to observe, something like "add OpenTelemetry so I can track this application's performance," and it turns that into an intent specification, then enriches your existing code with instrumentation to match. OpenTelemetry is open source and on GitHub. Pairing it with an AI agent that actually understands your codebase is what turns "add some metrics" into instrumentation that's useful.

Taylor Mattison explains how SigNoz MCP helped surface wasted work inside MSI's sales-order workflow. Warning checks were firing on user actions that had nothing to do with any warning they could raise. By comparing telemetry across the workflow, Taylor could point to unnecessary operations that were wasting API calls, database time, and server capacity. This clip is part of our MSI customer story on using SigNoz MCP with Claude to debug slow sales orders across the stack.