Runs, gates, and workflows: the primitives, explained
Most of what people mean when they ask "how does this actually work" comes down to three words used precisely: workflow, run, and gate. This is what each one is, and how they fit together.
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Watch this AI agent find and fix performance bottlenecks
Anyone can claim an AI agent will fix your performance problems. This demo shows exactly what it looks at and what it hands back. In this Product Highlights conversation, Sylvain Guittard, Senior Director of Product at Upsun who leads the team behind the Upsun console and CLI, runs the Upsun Cloud Performance Agent live on a demo project. His take: "You have a patch that is already available, and a recommendation, so you can see if it fits or not to your context." We get into.
This AI agent finds your app's bottlenecks and suggests the fix
Most teams collect the profiles and traffic data that explain a slowdown. Almost nobody has time to read it before users notice. In this Product Highlights conversation, Sylvain Guittard, Senior Director of Product at Upsun who leads the team behind the Upsun console and CLI, breaks down the Upsun Cloud Performance Agent, the first background agent running on Upsun Cloud. His take: "We monitor everything, we feed that into an agent, and the agent will be capable of finding what the bottlenecks are in your application. And on top of it, it gives you a patch, or a way to fix it." We get into.
Juggling AI tools works, until it does not
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.
Your AI stack will change again. Stop rebuilding it.
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.
Before your AI bottleneck gets worse: what to put in place now
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.
You made coding faster. Guess where the bottleneck went next.
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.
Your AI coding gains are stuck before the code is even written
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.
Agent access isn't workflow trust
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 customer stories: 96% less downtime, zero compliance shortcuts
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.