[amabito]

That’s actually a pretty disciplined setup.

What you’ve described sounds a lot like layered containment:

Loop budget (hard recursion bound)

Progressive checks (soft convergence control)

Sleep cycles (temporal isolation)

Deep sleep cap (bounded self-modification)

Git rollback (failure domain isolation)

Out of curiosity, have you measured amplification?

For example: total LLM calls per wake cycle, or per deep sleep?

I’m starting to think agent systems need amplification metrics the same way distributed systems track retry amplification.

[rsdza]

I haven't actually measured it, but that could be interesting to see over time!

So far it seems pretty sane with Claude and incredibly boring with OpenAI (OpenAI models just don't want to show any initiative)

One thing I neglected to mention is that it manages its own sleep duration and it has a 'wakeup' cli command. So far the agents (i prefer to call them creatures :) ) do a good job of finding the wakeup command, building scripts to poll for whatever (e.g. github notifications) and sleeping for long periods.

There's a daily cost cap, but I'm not yet making the creatures aware of that budget. I think I should do that soon because that will be an interesting lever

[rsdza]

I guess also worth mentioning is that the creatures can rewrite their own code wholesale, ditching any safety limits except the externally enforced llm cost cap. They don't have access to LLM api keys - llm calls are proxied through the orchestrator.