Operations | Monitoring | ITSM | DevOps | Cloud

AI coding tools were supposed to mean developers work less. On a recent webinar recorded with LeadDev, senior engineering manager Vernon put words to something a lot of teams are quietly noticing instead: “It’s concerning because it’s the opposite of what was promised. We were supposed to be working less.”

You’re probably running a TLS configuration that the IETF says is “non-conformant”. But you didn’t do anything wrong. In July, the IETF published a pair of RFCs that took away three of TLS 1.2’s key exchange methods and froze the rest of it. The phrase they used is MUST NOT, the strongest thing a specification is allowed to say. Nginx, Apache, and Windows Server all ship with those key exchanges turned on by default. Nothing breaks tomorrow.

AI agents are quickly becoming part of the enterprise automation conversation because, among other things, they help teams move faster. But there is a major difference between an AI agent that sounds useful in a demo and an AI agent that is ready for production. Production agents need scope. They need to know what they own, which systems they can touch, which workflows they can run, which teams they support, and where the boundaries are.

When we launched our Governance Gap series, we set out to explore how the explosion of AI-assisted engineering changes the risk profile for modern software organizations. We looked at the rise of The New Software Creator and analyzed why deployment governance is what keeps teams safe when code production accelerates. We also mapped out the realities of security at scale and defined who owns governance accountability.

Anthropic, the company behind Claude, ran the most honest experiment in agentic AI: they gave a version of their own model $1,000, a small fridge in the office lunchroom, and one job. Run a shop. Make a profit. They named it Claudius. Claudius did not make a profit.

Instead of building a separate role for every team, region, or department, admins can create a single role that automatically personalizes access for each user based on their SSO attributes. Someone moves teams or a new group gets created, and the new access takes effect at their next login with no CloudZero configuration. As AI spend grows, more companies are looking to give teams visibility into their own AI costs without exposing every individual’s usage across the org.

Splunk charges you in one of two ways: by how much data you send it each day, or by how much compute your searches and dashboards use. Security teams pay for both the platform and Splunk Enterprise Security, the app that turns Splunk into a SIEM, which is priced separately on top. This guide breaks down every part of a 2026 Splunk bill, works through a real, sourced pricing example, and lays out the ways to bring the number down, including the one lever many teams overlook.

Engineering organizations spent the past decade learning to measure developer and team productivity. Measurement of the organization itself did not keep pace. Now that agents are responsible for writing most of the code across many engineering organizations, it’s more important than ever to have an effective methodology for measuring productivity.

We shipped a chaos feature that never caused any chaos. Our mock server has had a fault-injection effect for years with a straightforward job: withhold the response entirely and see whether the caller copes. Last week I audited it against the actual code path. It had never withheld anything. The handler returned early without writing a response. Go’s net/http then did what it is designed to do, which is synthesize a 200 OK and flush the recorded body.