Operations | Monitoring | ITSM | DevOps | Cloud

This isn't a teardown. This stack is a genuinely reasonable way to start. An AI-first editor handles day-to-day writing. A terminal-based coding agent takes on tasks that need more autonomy: a full feature, a migration, a stubborn bug. A few scripts connect the pieces, trigger a run, and post a result somewhere. An observability tool checks what happened after the fact. Every part of that is a real, capable tool. For the first few months, on a small team, it works.

The model your team relies on today is unlikely to be the one you're relying on a year from now. If your team's process for shipping AI-assisted code is built around a specific model, coding assistant, or a vendor's take on an autonomous agent, you are not building infrastructure. You are building something you will tear out and rebuild the next time the leaderboard shifts.

Your engineers have agents running. Not one agent, but several, spread across the team. Some run in a terminal on a laptop, some are wired into your CI jobs, and some live inside whatever coding tool each person prefers. Each one got set up separately, by whoever needed it, in whatever way worked that week. That is the state most teams are in right now. Code stopped being the slow part a while ago.

Somewhere in the last year, your team's code output went up. Pull requests are opened faster. The backlog of small fixes and routine changes started clearing quicker than it used to. If delivery still feels roughly as slow as it did before, that's what happens when you speed up one part of a process without touching anything downstream of it.

At some point this year, you likely approved a request to expand AI coding tool access across the team. The pitch was straightforward: engineers write code faster, the team ships more, the investment pays for itself. The first half happened. Engineers are writing code faster. If you're now being asked whether the investment paid off, and you're finding the honest answer is more complicated than a yes, you are not alone, and you have not been sold something broken.

Several platforms now ship agents capable of investigating problems, proposing fixes, and deploying, with real permission models and sandboxing behind them. This is a genuine engineering achievement, not just a demo.

Two Upsun Cloud customers, two very different starting points. Easypara needed to stop losing revenue to outages. ROLLIN needed to stop losing time to manual compliance checks on regulated client work. Here's how each one solved it.

Upsun Dispatch is generally available to the public as of today. Our previous article explains what it is and why we built it. This round, we take you into the details of how it works, the primitives it consists of, and the functionality available right away. You'll also get a glimpse of our roadmap at the end of the article.

Most of what runs on a modern platform is built to stay up. Applications serve requests, workers process queues, and managed services keep databases and caches available around the clock. But a meaningful share of the real work does not fit that always-on shape.