Agent observability needs feedback to power learning

LangChain argues traces alone don't create learning loops; feedback signals (explicit, implicit, LLM-as-judge, rule-based) are needed. Learning happens at model, harness, and context levels, enabling SFT/RL updates and better scaffolding.