Operations | Monitoring | ITSM | DevOps | Cloud

Based on insights from The Intelligent Enterprise podcast, “The Evolution from Automation to Autonomy” Every couple of weeks, The Intelligent Enterprise podcast steps away from the day-to-day noise of enterprise life to explore big ideas from a fresh perspective. In one recent episode, the focus turned to a question many organizations are still grappling with: What does it really take to build an AI-powered enterprise that works with people, not against them?

Engineering organizations rely heavily on developer feedback to improve internal platforms, tooling, and processes. However, that feedback is often scattered across disconnected systems such as external forms, spreadsheets, chat threads, and documentation tools. Because these systems are separate from operational data, teams struggle to correlate developer sentiment with measurable performance or reliability outcomes.

Meta's internal token leaderboard sparked a frenzy — and a reckoning. Tokenmaxxing without attribution is just cloud waste 2.0. Companies like Hudl and Duolingo use cost intelligence to connect every AI dollar to a business outcome.

You probably have Datadog. Maybe New Relic, maybe Dynatrace. Your observability stack has been solid for years — and you're still flying blind on AI cost. Here's why LLM observability needs a fourth pillar most tools skip, and how to build one that actually tells you what your models are costing you per request, per feature, per customer.

DNS Spy now goes well beyond DNS record monitoring. We've shipped SSL certificate discovery and security auditing, expanded the Security Center to 40+ automated checks across six categories, and built expiration tracking for both domains and SSL certificates — with tiered alerts so nothing expires without warning.

In the modern software factory, 99.9% uptime is no longer the gold standard. A standard 99.9% SLA translates to approximately 43 minutes of unexpected downtime per month. While industry data shows that a single minute of downtime costs an average of $9,000, for large global enterprises, that figure can easily be 5x higher. At tens of thousands of dollars per minute, those 43 minutes quickly compound into a catastrophic financial and operational risk.

A lot of teams have a collection of Nagios plugins and custom monitoring scripts that have been running reliably for years. Some are standard community plugins for checking disk health or SSL certificate expiry. Others are homegrown Bash or Python scripts that check something very specific to the business: whether an API endpoint returns the right payload, whether a batch job completed on time, whether a queue depth is within bounds.

The redesigned Gemini Cloud Assist proactively executes tasks such as designing applications and optimizing costs that used to need human oversight.

CMake tends to have a bad reputation for being to complex and convoluted, but often that notion stems from very old versions of CMake. Sure, CMake is a Turing-complete scripting language, but that is really needed for an ecosystem as complex as that of C and C++. And as Greenspun’s tenth rule of programming goes: There are countless build-systems and build-system generators for the C/C++ ecosystem. Some of them tried to use a simple, declarative approach.