IDK why/how this works but by giving it a standardised set of apis to interface with the hardware (read logs, restart it and so on) we find models tend to behave much better ... it wont randomly do stuff it feels is appropriate for a debugging session.

At this point though our context is so dynamic because of lesson propogation its hard to think about a model on its own - a vanilla harness (which we were forced to use recently because the memories system went down for a bit) just felt so different!

I have the agent software set up to log every byte in and out of the model into a file, time stamped and which side of the conversation was active as well as all of the tool calls, parameters and stdin/out of the tools. Every week or so I start a job to process that log file and to extract useful lessons for future sessions. These go into a LESSONS.md file that gets read by every agent session on start-up.