Waves are one of the greatest sources of renewable energy and are a promising resource to tackle climate challenges by decarbonizing energy generation. Lowering the Levelized Cost of Energy (LCOE) for wave energy converters is key to competitiveness with other forms of clean energy like wind and solar. Also, the complexity of control has gone up significantly with the state-of-the-art multi-generator multi-legged industrial Wave Energy Converters (WEC). This paper introduces a Multi-Agent Reinforcement Learning controller (MARL) architecture that can handle these multiple objectives for LCOE, helping the increase in energy capture efficiency, boosting revenue, reducing structural stress to limit maintenance and operating cost, and adaptively and proactively protect the wave energy converter from catastrophic weather events, preserving investments and lowering effective capital cost. We use a MARL implementing proximal policy optimization (PPO) with various optimizations to help sustain the training convergence in the complex hyperplane. The MARL is able to better control the reactive forces of the generators on multiple tethers (legs) of WEC than the commonly deployed spring damper controller. The design for trust is implemented to assure the operation of WEC within a safe zone of mechanical compliance and guarantee mechanical integrity. This is achieved through reward shaping for multiple objectives of energy capture and penalty for harmful motions to minimize stress and lower the cost of maintenance. We achieved double-digit gains in energy capture efficiency across the waves of different principal frequencies over the baseline Spring Damper controller with the proposed MARL controllers.