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The latest News and Information on Observabilty for complex systems and related technologies.

Observability for the Agent Era: Day 2 | Launches

Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 2 of Innovation Week, Honeycomb's product and engineering teams will take you inside the new capabilities purpose-built for the agent era. Expect live demos, real scenarios, and a hands-on look at what it means to own observability for the Agentic era, with AI in Honeycomb to observe AI in production. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.

Honeycomb Innovation Week: Debugging Agentic Workflows with Ken Rimple

Canvas skills are how your team's runbooks and tribal knowledge become an active part of the investigation instead of a document someone has to remember to open. Pre-built skills cover the most common investigation patterns out of the box. Custom skills let you encode the specific context, thresholds, and decision logic your team has accumulated, so every auto-investigation starts with your best thinking already applied.

From Monitoring to Observability: How DEX Integrations Strengthen IT Visibility and User Productivity

When I started working in IT in the last 90’s, IT performance was always measured by the health of infrastructure: CPU utilization, network latency, server uptime, and for many organizations, little has changed in the last 30+ years. We became very good at keeping systems alive, yet users still struggled to get work done. That disconnect is exactly why Digital Employee Experience (DEX) has emerged as a critical discipline. But DEX on its own is not the end goal.

Observability for the Agent Era: Day 1 | Keynotes

Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 1 of Innovation Week, Honeycomb co-founders Christine Yen and Charity Majors will share what it actually takes to understand and debug systems in the agent era, and what the best engineering teams are doing differently. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.

Innovation Week Day 1: The SDLC Is Collapsing, and Observability Has Never Mattered More

The software development lifecycle is collapsing. The multi-stage pipeline that defined how software got built and shipped for decades is compressing into rapid loops of intent and validation, with agents now part of the teams building and running it. Day 1 of Innovation Week was about what that shift means for how software gets validated, where observability fits, and the problems that have always been hard but are now genuinely urgent.

Security Integrations in Observability Self-Hosted

Integrating security data with observability data provides a comprehensive view for better threat detection and response. Security observability helps connect the dots between seemingly innocent events that, when correlated, reveal complex attack patterns. SolarWinds security products integrate into observability self-hosted, including Security Event Manager for log data and event correlation, Access Rights Management for identifying potential attack vectors, configuration management for compliance monitoring, and Patch Manager for tracking critical updates.

Turn Noisy Logs Into Structured Data with Uptrace Grouping Rules

Here are 3 YouTube title options plus a description optimized for technical/dev audiences: Same log pattern. Hundreds of useless groups. In this video, we show how to use Uptrace Grouping Rules to automatically turn noisy logs into structured, searchable data — without changing application code. You'll learn how to: Examples covered: Perfect for:#OpenTelemetry users, backend engineers, SREs, and anyone dealing with noisy logs.

Making Semantic Conventions Work for You With OpenTelemetry Weaver

Your dataset has hundreds of attributes. Some are self-explanatory: http.response.status_code, server.address. Others are not: meta.refinery.reason, dataset.slug, sli.latency_target_ms. If you don't know what an attribute means, you can't write a good query. And if an AI agent doesn't know what it means, it guesses.