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

Best PR Review Tools for AI-Generated Code (2026)

Jul 20, 2026 By Lightrun Team In Lightrun

Most PR review tools answer one question: does this code look correct? They scan the diff, flag known patterns, catch security violations, and post inline comments before merge, which is useful, but only half the review. The other half is behavioral: does the code actually behave correctly once it’s running against your live system?

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Why partners love working with Cribl

Jul 20, 2026 By Cribl In Cribl

Hear directly from Cribl partners—including AWS—about what it’s really like to work together. This short is for technology and cloud partners, consulting firms, and customers who want a quick, human view of Cribl’s partner ecosystem and the value it delivers. In under two minutes, partners highlight Cribl’s partner program, the people they work with, and the outcomes they’re delivering for joint customers. You’ll hear about the FedRAMP opportunity, why “it’s all about the data” for AWS, and how Cribl helps get data where it needs to be for shared customers.

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30 to 70 PRs a Day: How We Managed to Not Wreck Our Systems

Jul 16, 2026 By Liz Fong-Jones In Honeycomb

In this two-part blog series, I give a detailed report-out on how our Honeycomb engineering team 2.5x-ed our throughput using AI without breaking everything or lowering our standards for quality. Part 1 explains how we did it and shows data about how that ramp-up happened. Part 2 shares what we learned.

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AI Amplifies Your Existing Practices: Lessons from Our Shift to an AI-First Strategy

Jul 16, 2026 By Liz Fong-Jones In Honeycomb

In this two-part blog series, I give a detailed report-out on how our Honeycomb engineering team 2.5x-ed our throughput using AI without breaking everything or lowering our standards for quality. Part 1 explains how we did it and shows data about how that ramp-up happened. In this blog, I share what we learned. The “platform engineering” frame and the “autonomy, ownership, feedback loops” frame are the same frame, spoken in two different vocabularies.

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Datadog named Leader in 2026 Gartner Magic Quadrant for Observability Platforms

Jul 15, 2026 By Datadog In Datadog

We are thrilled to announce that Datadog has been named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms, for the sixth consecutive year. We believe this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack. Datadog was positioned highest in Ability to Execute in the 2026 Gartner Magic Quadrant for Observability Platforms.

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Grafana Labs named a Leader again in the 2026 Gartner Magic Quadrant for Observability Platforms

Jul 15, 2026 By Robin Gustafsson In Grafana

We’re delighted to share that Grafana Labs has been named a Leader in the Gartner Magic Quadrant for Observability Platforms for the third consecutive year. Notably, we’re also positioned furthest in “Completeness of Vision” for the second year in a row.

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5 Things to Know About Context Engineering

Jul 15, 2026 By Mezmo In Mezmo

Software systems are getting better at understanding themselves. The mix of richer telemetry, smarter pipelines, and agentic AI is shifting observability from a passive record of events into something more active and useful. That shift is what we mean by context engineering. We recently partnered with O’Reilly on a report by David Beale that introduces the discipline. Before you read it, here are five things worth knowing.

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Transforming How We Run Kafka at Honeycomb

Jul 15, 2026 By Josh Parsons In Honeycomb

We recently wrapped up a large-scale, multi-month Kafka migration project. We used to run self-hosted Confluent Platform and ZooKeeper as clusters of AWS EC2 instances, and now all of our Kafka clusters run open-source Apache Kafka 4.1.1 running in KRaft mode and deployed to AWS EKS.

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What is AI cost observability? A guide to tracking LLM and AI spend

Jul 14, 2026 By Keith MacKenzie In CloudZero

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value.

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AI is Exposing Observability's Dirty Secret

Jul 14, 2026 By Speedscale In Speedscale

The 3 pillars of observability are breaking. For years, dev teams relied on Logs, Metrics, and Traces to know when something went wrong. But now? AI agents are writing, deploying, and changing code in real-time. When an AI hallucination pushes a bug to production, standard monitoring sees nothing wrong.To survive the AI era, we need a 4th Pillar of Observability. Watch to find out what it is and why the old way of monitoring just became obsolete.

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