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

Fewer Tools, Faster Fixes: A Practical Guide to Observability Consolidation

Most observability stacks aren’t designed, they accumulate. A logging tool here, a tracing platform there, and before you know it you’re managing rising costs and a setup that ultimately slows down your team. And you’ve moved further away from actually solving problems for your users.

ICYMI: Is This Code Worth Running? Here's How to Know

Over the last three months, we’ve been exploring what about software development and observability changes with AI, and what doesn’t. Our conclusion: these five principles will still remain true, even when 90% of the code is AI-driven. The agentic AI space is moving fast. Models are improving, context windows are expanding, and the ways people build and operate agents are changing so fast that any thoughts we share could feel dated by the time you read this.

Optimizing the OpenTelemetry Python SDK for LLM Workloads

Agentic workloads thrive with precision tooling. Just like developers, they need the rich context, high cardinality, and fast feedback loops that allow them to ask exploratory open-ended questions of their code. But instrumentation is costly, and from the dawn of software, developers have tried to do the most possible with the least amount of resources.

Top 6 AI SRE Tools and Why Runtime-Grounded Reliability Is the New Standard

AI SRE tools accelerate incident detection, root cause analysis, and remediation across distributed production systems. They ingest telemetry signals, including logs, metrics, traces, alerts, and deployment history, to correlate anomalies, narrow fault domains, and reduce manual triage. This guide breaks down the top AI SRE tools in 2026 and helps you choose the right one based on your team’s biggest bottleneck, whether that is faster triage, deeper root cause analysis, or runtime-level validation.

Beyond the Dashboard: Selector's Patented Approach to Conversational Observability

For years, IT operations teams have been trapped in a frustrating paradox: the data they need to solve critical issues is right at their fingertips, yet entirely out of reach. Accessing it requires engineers to master complex, platform-specific query languages, dig through endless layers of dashboards, and hunt for the exact visualization that holds the answer. Under the intense pressures of modern speed, scale, and complexity, this rigid model is breaking down.

Tech Talk | AI Agents in O11y Cloud

Transform reactive incident response with Splunk’s troubleshooting agents, designed to drastically reduce mean time to identify and resolve issues. This session demonstrates how a multi-agent approach empowers teams of all skill levels to pinpoint root causes, prioritize issues by business impact, and prevent future outages. Tech Talk sessions offer insightful and valuable deep-dives for any technical practitioner.

Telegraf Controller and Agent Observability

Telegraf Controller makes it easier to manage and monitor your Telegraf agents in one place. In this overview, Product Manager Scott Anderson explains how it works. Agents pull their configurations directly from the controller and report their status back using a heartbeat plugin. This gives you a clear, real-time view of your deployment health. You can quickly see how everything is running at a high level or drill into individual agents for more detail. It's a simple way to stay on top of large Telegraf setups.

When Your Observability Literally Stops Traffic

Last week, a fleet of autonomous robotaxis in China suddenly stopped working—at scale. Over a hundred vehicles stalled across a city, stranding passengers in traffic and raising immediate concerns about safety, reliability, and trust in autonomous systems. This wasn’t just a bad day for self-driving cars. It was a distributed systems failure, one that happened in the physical world, not just in dashboards.

Uncertainty and Change Are Everywhere in Software Development

If you’re like everyone else who works in software development, it’s a good bet that almost every single thing that you thought you knew about your business and engineering has changed as a result of the advent of modern LLMs. How should you respond to these changes? How should you change how you and your team develop software?