Operations | Monitoring | ITSM | DevOps | Cloud

A practical guide to risk-based code review

Traditional code review no longer keeps pace with how much code teams are shipping. Risk-based code review is the response: instead of giving every pull request the same scrutiny, you route human attention by risk, letting low-risk changes ship with light or automated review and reserving deep human review for the changes that are expensive to get wrong.

Just ask AI to add OpenTelemetry to your code

OpenTelemetry instrumentation used to mean hours of manual work, wiring up metrics service by service. Now you can just ask for it. Tell an AI agent what you're trying to observe, something like "add OpenTelemetry so I can track this application's performance," and it turns that into an intent specification, then enriches your existing code with instrumentation to match. OpenTelemetry is open source and on GitHub. Pairing it with an AI agent that actually understands your codebase is what turns "add some metrics" into instrumentation that's useful.

The 2026 pocket guide to engineering metrics

Most engineering leaders are drowning in data but starved for insight. We have dashboards full of metrics, but they often create more questions than answers and rarely tell us what to do next. In the age of AI, where development velocity is accelerating at an unprecedented rate, this problem is only getting worse. Shipping code faster than you can fix it is an existential risk, and a dashboard that doesn't lead to action is just a distraction.

Your platform is your business, encoded onto your infra, with Syntasso's Abby Bangser

Cortex co-founder and CTO Ganesh Datta sits down with Abby Bangser, a platform engineering leader at Syntasso and former lead of the CNCF Platforms Working Group, to talk about why AI agents need real platform APIs, not raw cloud credentials.

AI didn't take humans off the platform, it just changed the job they do there.

Agents are writing more of the code these days, but that doesn't make them the only user of your platform. Abby Bangser, Principal Engineer at Syntasso and CNCF Ambassador, makes the distinction: the agent might be your primary coder, while humans are still validating what it builds and interacting with the system it runs on. From a Braintrust conversation with engineering leaders on AI agents and engineering operations.

DRIVE Deep Dive: Efficiency

This is the fifth and final post in the DRIVE Deep Dive series, following Delivery, Reliability, Initiatives, and Vigilance. For the complete model across all five pillars, download the full DRIVE framework. -- Engineering money and time land in three places a leadership review can actually act on: the cloud bill, the internal spend on AI and LLM tokens, and the split between building new things and keeping old ones running.

Your platform team isn't a ticket queue, here's the fix.

Centralized ops creates a single point of failure: every request waits in line, and your best engineers spend their day gatekeeping instead of building. Self-service APIs change that. Developers get what they need without filing a ticket, and platform teams get their time back for the infrastructure work that actually moves the needle. Still routing everything through one central team? Tell us your setup in the comments.

Cortex | Enhanced Filters in Engineering Intelligence

Filtering in Engineering Intelligence just got a lot more precise. In this Feature Friday, Principal Product Manager Christine Byun walks through the enhanced filters now live across the platform, using PR cycle time in the Data Explorer as an example. What's new: Try it out in Engineering Intelligence today.

Cortex Platform Walkthrough

In this video, Becka gives a guided tour of Cortex, the Engineering Operations Platform that runs mission control for your AI software factory. Learn how to centralize visibility, clarify ownership, and automate standards across your entire software ecosystem. What we cover: Why Cortex: Cortex is where engineering leaders run mission control for the AI software factory: the visibility, intelligence, and controls to keep teams shipping fast without letting accelerated output turn into accumulated risk to reliability, security, and cost.