Operations | Monitoring | ITSM | DevOps | Cloud

Most cloud-native cost tools are built to find and address waste reactively. Underutilized nodes, oversized requests, and idle workloads are revealed in the utilization data, the fixes are well documented, and the initial savings these tools drive are very real. But what we’ve seen consistently across clusters is a different category of blocker, one that quietly prevents consolidation and strands capacity your autoscaler can never reach. They don’t surface in dashboards as obvious waste.

Organizations are increasingly using multiple models to build AI agents in order to find the best balance of performance and cost for each agentic task and LLM call. As we discovered in the 2026 State of AI Engineering report, there isn’t currently a clear winner in terms of adoption among competing models and many organizations are keeping older models in flight despite frequent new releases.

Since November, Kosli’s own engineering team has been running a live experiment: what happens to code review when the thing generating the code - and increasingly, the thing reviewing it - is an AI, not a person. Alex Kantor, Kosli’s Director of Technology, walked through that experiment in this webinar: what broke, what it cost to fix, and what four “obvious” assumptions in a standard code review control turned out not to hold once you took the human out of the loop.

As we entered 2026, enterprises raced to build internal AI engineering agents, automating incident response, code review, and support. The investment was real, but 88% of these pilots never reached production, and teams are now in rebuild mode, trying to understand why. Live runtime validation was the key architectural decision skipped in these v1 agents and it’s still missing from many v2 designs. Agents need to verify their reasoning against production before they act.