‘Agentic gradient’ would let AI agents change their own harnesses or weights

A user calls the proposed algorithm “agentic gradient”: an agent could change its own harness or weights. They say getting it to work remains a meta-reinforcement learning problem with many hurdles. In a follow-up, the same user says they are mostly convinced engram modules, or something like them, will be fundamental to continual learning rather than just an efficiency trick.