Abstract: In order to meet the need for scalability in academic advising in higher education settings while ensuring compliance, fairness, and privacy, we propose a governance-ready multi-agent reinforcement learning framework that aggregates the advantages of four highly specialized agents, namely, the Plan Validator, Risk Detector, Resource Recommender, and Conversational Advisor, which function collaboratively with a policy learner and under the supervision of a human. In a quasi-experimental mixed-methods study involving n = 345 participants from different departments, the system yielded a substantial improvement over the baseline in terms of efficiency and quality, where the turnaround time in academic advising was reduced by ≈60% or from 48.6 to 19.3 hours, while compliance was boosted by +11.3% and risk detection recall was enhanced by +0.12 or from 0.67 to 0.79. The stability of the learning process was satisfactory, with a normalized cumulative reward of +0.35 and around 1,200 episodes to converge. For the governance control, the effectiveness of the proofs was satisfactory, as the fairness gap reduced from 7.8% to 3.2%, while the privacy incidents remained zero even with continuous audits. In terms of evaluation, a mixed methods evaluation design was used, which included the performance records of the RL, ANOVA subgroup, and trust judgments to establish validity. From the findings, the MARL solution is technically viable, ethical, and feasible as an advisory mechanism in HEIs. Suggestions are made for future directions to investigate the solution's scalability and dynamic reward shaping for various policies.