Language-model agents increasingly act through tools, APIs, databases, and other stateful environments, where task success depends on external evidence rather than final text alone. Existing reward paradigms face a trade-off: preference and rubric-based feedback are flexible but often weakly grounded, while verifier-based rewards are grounded but typically tied to narrow task-specific checkers. We argue that agentic reinforcement learning needs reward interfaces: mechanisms that explicitly connect task requirements to environment evidence, provenance, and reward aggregation. We introduce **Agentic Rubrics**, an executable reward-interface view with four components: criteria, evidence acquisition, provenance, and aggregation. This view separates what should be evaluated from how evidence is obtained, audited, and converted into reward. We also specify a minimal environment contract for auditable reward construction and give an illustrative instantiation in stateful tool-use environments. In this case study, Agentic Rubrics augment final environment feedback with process-aware rewards derived from tool traces and state records, yielding modest improvements in task performance and process reliability while revealing domain-dependent limitations. These results support the position that grounded agent learning should treat reward construction as an interface problem, not only as scalar reward modeling.