DRIVE Deep Dive: Initiatives
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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How to Trace Microservices WITHOUT OpenTelemetry
Learn how to trace complex multi-hop transactions across microservices when OpenTelemetry isn't available or trace parents get lost. This quick tutorial demonstrates how to use full-text search in proxymock (a free CLI and UI tool) to isolate user journeys (like looking up a specific username) and instantly generate waterfall tracing views. Whether you are working locally or in a Kubernetes cluster, you can record API traffic, visualize multi-hop requests, and easily transition those captures into load or regression tests.
DORA measures delivery. DRIVE measures the organization behind it.
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.
DRIVE Deep Dive: Reliability
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.
DRIVE Deep Dive: Delivery
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.
Cortex catalog data now flows into Rootly
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.
What is an AI software factory?
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.
So you need to add microcontrollers to your fleet: now what?
Your Ubuntu Core fleet is running beautifully. OTA updates roll out in minutes. Every device is strictly confined, cryptographically attested, and carrying a 10 to 15 year long term support (LTS) commitment. The operational team sleeps soundly. Then the product roadmap meeting happens. The industrial floor needs vibration sensors on every motor. The smart building needs temperature nodes in every room. The cold chain system requires dozens of low-power Bluetooth tags. And someone just said the words.
How to run an operational excellence review for software engineering
Most engineering organizations already run something they call an operational review. It usually looks like a cousin of the quarterly business review: a deck assembled every few months, walked through team by team, anchored on whatever incidents happened to land in the previous quarter. By the time leadership sees the data, the systems it describes have moved on and the next set of risks is already accumulating in the gap.
Measuring engineering organizations in the age of AI
Engineering leadership is in the middle of a real transition, and most of the leaders I talk to know it. AI has reshaped how software gets built quickly enough that the operating models many of us spent a decade refining no longer fit cleanly, and there is a great deal of serious work happening across the industry to figure out how these models should evolve. The teams I find most impressive right now are the ones treating their operating model as an open question rather than a settled one.