Everyone talks about "agentic loops" but most of them are open loops in disguise. The agent does something, the user checks it, the user fixes it, and the cycle continues with the human doing most of the heavy lifting.
A truly closed loop means the agent can verify its own work. It means there's a feedback signal that flows back from the result to the agent, informing the next action. This is harder than it sounds.
Consider a simple task: "fix this failing test." An open-loop agent reads the test, guesses at a fix, and hands it back to you. A closed-loop agent reads the test, applies a fix, runs the test, reads the output, and iterates until it passes. The difference isn't intelligence — it's architecture.
We've been building tools that make it easier to close these loops. The key insight is that most of the infrastructure already exists. CI pipelines, test suites, linters, type checkers — these are all feedback signals. The challenge is connecting them to agents in a way that's fast enough to be useful.
Speed matters more than you'd think. A loop that takes 30 seconds to close feels interactive. A loop that takes 5 minutes feels like batch processing. When agents can iterate in seconds, users start trusting them with bigger tasks.
We're still early in figuring out what the right abstractions look like. But we're convinced that the future of useful AI isn't smarter models — it's tighter loops. The model that can try, fail, and try again in seconds will beat the model that gets it right on the first attempt but takes minutes to respond.
That's the bet we're making at Loopwork, and everything we ship is in service of making loops close faster.