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By Cristina Buenahora
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.
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By Cortex
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.
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By Ganesh Datta
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.
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By Ganesh Datta
This is the fourth post in our DRIVE Deep Dive series. Over the coming weeks we're examining each pillar of the DRIVE framework in turn. For the complete model, download the full DRIVE framework. Our last post covered Initiatives. Up next: Efficiency. The bottleneck on writing code is gone, and the industry is responding the way it always does when a constraint disappears: by producing more.
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By Ganesh Datta
This is the third post in our DRIVE Deep Dive series. We've covered Delivery and Reliability so far; this post takes on Initiatives. For the complete model, download the full DRIVE framework.
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By Cortex
You can hit every DORA target and still be losing ground. Deploy frequency up, lead time down, change failure rate and MTTR both healthy, but underneath it could be hiding an organization slowly getting worse at turning work into reliable software. New services shipping without clear owners, an attack surface widening faster than anyone is tracking, on-call rotations quietly filling up, a migration that was supposed to close last quarter still limping along.
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By Ganesh Datta
This is the second post in our DRIVE Deep Dive series. Over the coming weeks we're examining each pillar of the DRIVE framework in turn, and mapping DRIVE against the frameworks engineering leaders already run on, including DORA and SPACE. For the complete model, download the full DRIVE framework. Our last post covered Delivery. Up next: Initiatives. --- AI has removed the last real constraint on producing code, and organizations are shipping more of it than ever.
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By Ganesh Datta
This is the first post in our DRIVE Deep Dive series. Over the coming month we'll examine each pillar of the DRIVE framework in turn, and map DRIVE against the frameworks engineering leaders already run on, including DORA and SPACE. For the complete model, download the full DRIVE framework.
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By Skyler Wuolle
Incident response is a context problem. The first minutes of any incident are spent reconstructing what the affected service is, what it depends on, and who owns it. That reconstruction happens during the worst possible window. The Cortex catalog already holds this data: services, teams, domains, and the relationships between them, maintained by the engineers who run those systems.
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By Ganesh Datta
Ask a software engineer what they do and the answer, for years, has been some version of "I write code." That assumption is unwinding fast. AI agents can now write code, review pull requests, run tests, and ship to production, and they're taking on a fast-growing share of that work. As agents absorb more of the execution, the human role shifts.
Steve Flanders (Senior Director of Engineering, Splunk) makes the case that AI can't save an observability stack that never agreed on a standard. Mix formats across metrics and logs, and AI stops correlating and starts guessing, which means you either make the wrong call or miss the answer you actually needed. OpenTelemetry is one fix, but Prometheus and Fluentd work too. The standard matters more than which one you pick.
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.
Search in Catalog used to mean typing an exact name and hoping. Lead Platform Engineer Keith Zeto walks through the rebuild for this Feature Friday.
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.
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.
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.
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.
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.
The World Cup is down to the final two, and so is Cortex's Coming Home Dashboard. In this Feature Friday, VP of Strategic Initiatives & Marketing Cristina Buenahora walks through an update on the Coming Home Dashboard she and Edward Spencer built a month ago, and the product update behind it. What she covers: Whether you're tracking brackets or tracking engineering maturity, the view's the same. Reach out to your CSM to try it.
Cortex co-founder and CTO Ganesh Datta sits down with Dr. Martin Nettling, Senior Director of Engineering and Head of QA at Tealium, to explore the distinction between trusting people and having confidence in tools, and why that difference matters as AI becomes part of every engineering workflow.
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Cortex makes it easy for engineering organizations to gain visibility into their services and deliver high quality software.
Cortex helps engineering teams build better software at scale:
- Align your team and drive accountability: Scorecards enable teams to drive what matters most to them – including service quality, production readiness standards, and migrations.
- A single source of truth for your services: Cortex’s service catalog integrates with the most popular engineering tools, giving teams an easy way to understand everything about their architecture.
- Build a culture of reliability and high performance: Teams enable organizations to drive a sense of ownership and pride as they improve service quality.
- Ensure new services follow best practices from day one: Scaffolder lets developers scaffold a new service in less than five minutes using custom templates crafted by your team.
Cortex gives organizations visibility into the status and quality of their microservices and helps teams drive adoption of best practices so they can deliver higher quality software.