Comment on: Safety policy for constraining meta-agent modifications

The safety policy pack addresses the right constraints — scoping writes to `workspace/`, approval gates for evaluation functions, and preventing self-rewriting of the meta-agent's own code. One gap this doesn't cover: **behavioral drift detection during the optimization loop itself**. A meta-agent that stays within the write constraints but gradually shifts its optimization objective is harder to catch with static policy rules alone. Consider: the meta-agent is allowed to rewrite task agent source (within workspace/). Over N iterations, it could incrementally shift the task agent's behavior in ways that are individually within policy but collectively represent a significant drift from the original objective. Each diff looks safe. The cumulative trajectory is not. A complementary layer to the static policy: ```python @safety_constraint def behavioral_consistency_check(iteration: int, meta_agent_state: dict): """ Compare current optimization trajectory against baseline. F...