Operations | Monitoring | ITSM | DevOps | Cloud

Here's a scenario that's playing out in engineering teams across the industry right now. A developer uses AI to rapidly prototype a microservice. The code works. They deploy it to production. Six months later, something breaks. The system is under load, a database connection pools, and the service starts failing in subtle ways. The engineer pulls up the code, but here's the problem, they didn't write it. An AI assistant did. They don't understand the flow deeply. They don't know where to look first.

Most teams judge their AI coding agent on two things: the monthly bill and a feeling. The bill tells you what you spent and the feeling tells you whether it seems to be helping, but neither one tells you what the agent actually did. As these tools move into the critical path of how software ships, that gap is starting to matter. I wanted to replace the feeling with something I could measure and understand what shapes of work affects this bill, so I decided to run an experiment on myself.

Improved IT asset management, AI-powered actions, and usability improvements for Alloy mobile apps.

Point A: 1835. Andrew Carnegie is born in a weaver’s cottage in Dunfermline, Scotland. The cottage has one main room, which the Carnegies share with another family. Point B: 1901. Andrew Carnegie becomes the richest man in the world when Carnegie Steel Company wins the Iron vs. Steel industrialists’ war, and he sells the company to J.P. Morgan for the modern equivalent of $450 billion.

Reliability guardrails help make sure that your applications stay reliable without slowing down. In an earlier blog, we went into why agentic AI development needs reliability guardrails. It went over how the increased speed of AI development demands automated guardrails to verify resilience and what kinds of tests these guardrails should cover. But that’s only the beginning. By themselves, guardrails act as a gate to ensure resilience mechanisms hold under rapid changes.