arXiv:2610.30359v1 Announce Type: new Abstract: Fairness-aware recommender systems often encode one fairness objective during training or apply a fixed re-ranking rule after scoring. In deployment, however, the appropriate fairness action can vary across requests because the candidate pool, item metadata, exposure debt, user preference history, and utility budget all change at runtime. We propose FAIRGate, a model-agnostic delivery controller that places an agentic policy-selection layer between a base ranker and the final top-K list. The agent does not define fairness objectives from scratch. It chooses from a predefined policy library covering long-tail exposure, item-group exposure balance, concentration control, calibration protection, and abstention. A deterministic executor then applies the selected policy through constrained repair, and a validator accepts the result only when relevance, feasibility, metadata, calibration, and audit constraints are satisfied. The final language-model component receives only the verified audit trace and produces evidence-linked narration after the ranking decision is fixed. This design gives the agent runtime responsibility while keeping fairness enforcement measurable, bounded, and replayable.