Operations | Monitoring | ITSM | DevOps | Cloud

When something goes down, the last thing you want is to alt-tab between a monitoring dashboard, your on-call tool, and three Slack threads to figure out what is happening and who owns it. That context is usually all there. It is just scattered. The Hyperping MCP server fixes that by putting your monitoring data inside the AI tools you already work in. Your agent can read monitor state, outage timelines, SLAs, and on-call schedules, and answer the questions you would normally chase across tabs.

In our previous post on AI tokenomics, we looked at the rising cost challenge behind token-based AI systems. When enterprise IT teams rely on AI to reason through the same repeatable work over and over again, the costs to resolve those tasks may increase to an unreasonable level. That is where a deterministic IT automation platform becomes essential. A deterministic workflow follows predefined logic, meaning that given the same inputs and conditions, it produces the same expected result.

AI coding assistants like Claude, Cursor, Codex, GitHub Copilot have become standard tools in the modern engineering workflow. Developers use them to write code, generate tests, and review pull requests. But when something breaks in production, these assistants hit a wall: they have no access to your actual system state. They can reason about logs, traces, and metrics. They just can't see yours.

A year ago, the sentence “I just deployed an app on GitHub” wouldn’t have made sense coming from me. I’m the VP of People at CloudZero; code deployments and I were not close friends. That’s changed. In this AI era, non-engineers are building, and I think that’s a genuinely good thing. But only if it’s tied to something that matters.

If you run Claude through LiteLLM, some of that spend is probably going uncounted – and you can’t see it, precisely because the data isn’t there. Routing through a gateway is messier than it looks: LiteLLM alone can carry Claude several ways – the OpenAI-compatible endpoint, and the Anthropic pass-through proxy that the native SDK and Claude Code use – and each path describes the same call differently.

At O11yCon, we talked to engineering teams across the industry, and the numbers are starting to get genuinely wild: Mixpanel DevOps Engineer Eddie Bracho told us their engineering team is generating 50% more PRs than before AI came into the mix (sorry). That kind of velocity is exciting, but it's also a pressure test for every part of your stack that isn't writing code, including your observability practice. Here's what we're hearing from customers about how that's playing out.

Developer experience, commonly known as DevEx, describes how an organization’s systems, workflows, tools, and culture affect developer productivity. A positive DevEx leads to tangible organizational benefits, including faster releases, increased innovation, and reduced technical debt. Measuring DevEx enables engineering management to quantify their team’s impact and understand where to direct improvement efforts.

Coding agents like Claude Code, Cursor, and Codex CLI handle the coding parts of building an AI application well. The harder work comes after: understanding why a response went wrong, building eval sets that reflect real production behavior, and keeping up with an application that changes faster than any one-off script can. Teams spend 60–80% of their time on evaluation and error analysis, and much of that work needs to be redone every time the stack shifts.

More code is being written right now than at any point in our industry’s history. A year’s worth of software is now created every month, and most of it is no longer written by people. GitHub COO Kyle Daigle recently said, “There were 1 billion commits in 2025.