Operations | Monitoring | ITSM | DevOps | Cloud

Kentik CEO and co-founder Avi Freedman explains why observability is not enough in the age of AI.

You write code. Open a PR. CI runs. PR merges. Prod’s on fire by 5pm. Maybe you skipped writing some tests. (It's tedious, sometimes unclear, and easy to ignore when you're racing to ship—until something breaks and you realize a test could’ve saved your Friday night.) Maybe the PR review was more of a drive-by from a teammate who barely had time to skim the diff. But reviews and tests matter.

AI has become a massive investment for companies. Engineering teams across industries are integrating AI into their products, whether it’s through homegrown, self-managed models or third-party model integrations. But no matter how much AI shifts the user experience, it’s still an application, which means your engineering team still needs to operate it and keep it reliable. At the same time, AI applications add complexity and complications that require a shift in your approach.

Large Language Models (LLMs) are becoming increasingly accessible, with regular adoption of open-source models and the growing ecosystem of tools for running them locally. Compact versions are now able to run on consumer-grade hardware, so developers are using LLMs on personal devices like Linux workstations, macOS laptops, or even Windows machines. As this trend grows, so does the need to ensure that your LLM-powered applications run reliably across all major operating systems.