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The latest News and Information on Distributed Tracing and related technologies.

Bringing the Most Advanced Sampling to the OpenTelemetry Collector

Sampling is a core skill that everyone who runs an observability pipeline at scale will learn. There are lots of tradeoffs within the various decisions you'll make from reducing bandwidth, CPU, and memory, to reducing costs and making the observability backend's performance better for users. Historically, there have only been three mechanisms, each with their own tradeoffs: However, there is a secret fourth option: adaptive tail sampling—which changes those tradeoffs.

From traces to experiments: A loop for improving AI agents

Let’s say your team shipped a support agent last quarter. The launch demo went well, stakeholders were pleased, and everyone moved on. A few months later, things start to look off. Summaries of long conversations are truncated, and monitors show latency spikes on tool calls to the billing API. Your team’s first instinct is to ship fixes such as tweaking prompts or upgrading the model.

Control trace volume with OpenTelemetry tail-based sampling

OpenTelemetry (OTel) tail-based sampling helps teams control trace volume by retaining errors, slow requests, and other traces worth investigating while dropping lower-value traffic. In distributed systems, a single request can fan out across many services, each emitting spans. That volume adds up quickly. Some applications produce millions of traces per hour, while large clusters generate more than 10 billion spans per day.

GitHub Copilot Monitoring & Observability with OpenTelemetry

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.

Grok Build Observability with OpenTelemetry

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.

From alert to answer: a hands-on investigation with trace analysis in Mezmo

Authored by Sven Delmas, VP of Research at Mezmo I wanted to know what Mezmo's new trace features feel like with real telemetry behind them, so I built the smallest honest rig I could: the OpenTelemetry demo application running in a local Kubernetes-in-Docker cluster on my machine, one collector, and one deliberately simple Mezmo pipeline.

Debug AI agents wherever they run, from Slack bots to code review with Sentry's Agent Tracing

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.

SigNoz Cloud Dashboard Schema Is Now Built for AI Agents

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 Redesigned the SigNoz Trace View for Million-Span Traces

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.

OpenTelemetry at the edge: Observability for IoT fleets with Bindplane and Dynatrace

By the time an IoT device shows up in an incident review, it has usually already done its damage. Not the dashboard-gap kind. These devices are load bearing. They sit in the control path of substations, haul trucks, pump stations and cold rooms, so when they go blind the blast radius gets measured in tripped relays, spoiled stock, and unplanned outages rather than in missing datapoints.