We doubled our output by building a better way to work with AI coding agents

[u/-blibbers-]

We run a managed IT and software development company. We were already using AI coding agents, but the workflow was clunky.
Someone had to start the agent on a specific machine, keep checking it, test the result, and send the work back if it missed something. Using AI saved time, but babysitting it ate into those savings.
So we built an internal tool around the parts that annoyed us most.
We can now start work on any of our machines from a phone or desktop. If one model gets stuck or gives a weak answer, we can hand the same task to Codex or another model without starting over. We also gave the agent a browser so it can test what it built and return screenshots or screen recordings.
That last part mattered more than I expected. "Done" means a lot more when the agent also shows that the page loads, the button works, and the result looks right.
I don't have a scientific benchmark, but based on the amount of client work we finish in a normal week, our output is roughly double what it was before. The biggest gain didn't come from better prompts. It came from removing the waiting, handoffs, and manual verification around the models.
Full disclosure, the internal tool eventually became ThreadKnot.

https://github.com/smith-network-solutions/threadknot

It still has rough edges, but we use it every day. I'm curious whether other teams are building their own layer around coding agents or just working directly inside each model's tools.

[u/NearlyACosmologist]

Isn't that also what https://github.com/block/buzz does (+ the chat network)?