Comment by visarga

2 months ago

I made one similar harness, mine does lightweight sandboxing with Seatbelt on Mac and Bubblewrap on Linux. I initially used Docker too, but abandoned it. I like how these 2 sandboxes allow me to make all the file system r/o except the project folder which is r/w (and a few other config folders). This means my code runs inside the sandbox like outside, same paths hold, same file system. The .git folder is also r/o inside sandbox, only outside agent can commit. Sandboxing was intended to enable --yolo mode, I wanted to maximize autonomous time.

Work is divided into individual tasks. I could have used Plan Mode or TodoWriter tool to implement tasks - all agents have them nowadays. But instead I chose to plan in task.md files because they can be edited iteratively, start as a user request, develop into a plan with checkbox-able steps, the plan is reviewed by judge agent (in yolo mode, and fresh context), then worker agent solves gates. The gates enforce a workflow of testing soon, testing extensively. There is another implementation judge again in yolo mode. And at the end we update the memory/bootstrap document.

Task files go into the git repo. I also log all user messages and implement intent validation with the judge agents. The judges validate intent along the chain "chat -> task -> plan -> code -> tests". Nothing is lost, the project remembers and understands its history. In fact I like to run retrospective tasks where a task.md 'eats' previous tasks and produces a general project perspective not visible locally.

In my system everything is a md file, logged and versioned on git. You have no issue extracting your memories, in fact I made reflection on past work a primitive operation of this harness. I am using it for coding primarily, but it is just as good for deep research, literature reviews, organizing subject matter and tutoring me on topics, investment planning and orchestrating agent experiment loops like autoresearch. That is because the task.md is just a generic programming pipeline, gates are instructions in natural language, you can use it for any cognitive work. Longest task.md I ran was 700 steps, took hours to complete, but worked reliably.