Re-grounding Relational Experience under Changing Authority

An agent can learn a useful workflow from other people without acquiring their authority to execute it. When its role or authority changes, which parts should it reuse? We introduce ReGround, a system that re-grounds past experience in the acting agent's current position. Its main algorithm constructs a reasoned realization gap: unmet requirements, their blockers, and constraints on completion. This gap guides retrieval, reuse, and search for a permitted way to achieve the original goal. Directed memory preserves experience, while a second algorithm extracts candidate authority changes from language and checks them against supporting evidence. On a synthetic planning benchmark, our planner uses 48.2% fewer transition attempts than Strong Repair on 216 feasible cases, with equal completion. Across all 432 cases, however, it uses 2.72 times as many attempts. On the supplied single-goal benchmark, our planner finishes every feasible case in its first retrieved slice; repeated unsuccessful searches accumulate cost on external-dependent cases. In a multigoal diagnostic, unmet-requirement retrieval reduces search relative to goal-only retrieval, although Strong Repair completes more tasks. In the tested API configuration, normative grounding improves permission-query accuracy from 90/106 to 103/106 at higher token cost, but performs worse on the tested local model. Unified and separated memory implementations with the same typed semantics tie; role-conditioned prompting still misapplies experience. The results show conditional search savings and failures in search allocation and language interpretation.