Operations | Monitoring | ITSM | DevOps | Cloud

We just wrapped O11yCon 2026, and this year's conversations hit differently. Agent-based software development is here, now. It's no longer an optional choice, and everybody is struggling to understand what their agents are doing and how to make them cost less and perform better. Over the course of fifteen talks, we saw clearly that the old assumptions on how and who (or what) writes our software has been upended. Here are some highlights. We'll have videos available in the near future.

You already instrumented the backend with OpenTelemetry. Your services emit spans. Your teams know the OTel APIs. Maybe you already run a Collector. So when you start evaluating Sentry, the obvious question is: Do you need to replace your OpenTelemetry setup with the Sentry SDK? No. The practical answer is usually: keep OpenTelemetry where it already works, add the Sentry SDK where it gives you more application context, and send OpenTelemetry Protocol (OTLP) events to Sentry.

In The Five Pillars of AI Agent Accountability: A Diagnostic Framework for Engineering Leaders, we walked through each pillar of AI agent accountability (traceability, authorization provenance, identity and ownership, policy at scale, and human oversight) and argued that most enterprises today sit at Level 0 or Level 1 of the Accountability Maturity Model. The most common reaction we get when we share that framework is some version of: “We’re already covered. We have network policies.

Engineering leaders need more than raw metrics, they need actionable insights they can trust with their data. When evaluating engineering intelligence platforms, privacy controls and centralized repository oversight should top your criteria list. The platforms on this list each offer distinct approaches to tracking DORA metrics, developer productivity, and code quality while keeping your data secure.

Tracking down who made a specific change in your codebase can feel like detective work. Whether you’re debugging an issue or trying to understand why a particular piece of logic exists, knowing the history behind each line is invaluable. GitKraken makes this process simple with tools like GitLens for VS Code and GitKraken Desktop, which bring blame annotations directly into your workflow.

Your pull request has been open for three days. Your reviewer hasn’t commented. You’re starting to wonder if anyone will ever look at it—and whether the code you wrote on Monday still makes sense on Thursday. This feeling is common. PR lifecycle time—the duration from first commit to merged code—directly impacts how quickly you ship features, how fresh your code stays, and how engaged your reviewers remain.

Builder in the Loop is a Mezmo interview series focused on the engineers, product leaders, and operators shaping AURA, an open-source, MCP-native agent harness for production operations. The goal is to get past the polished product layer and talk through the decisions that matter when AI starts interacting with real systems. Key questions include: What should agents be allowed to do? How do they get better over time? Where should humans stay in the loop?

Key Takeaway: Harness AI Test Automation now runs existing Playwright suites without code changes, adds AI-powered failure triage, and integrates test results directly into build and deployment pipelines. ‍

Today, Canonical announced the release of Workshop, a solution for launching development environments with a single command. These environments are configured once, and can be reproduced on different machines. This means consistent workflows across development machines and deployment pipelines, and less time managing dependencies.