Development of SHEPI as an advanced artificial intelligence agent in the field of logistics revealed unique challenges to the construction team. Engineers throughout the project faced issues such as managing complex interactions between modules, optimizing real-time decision making, and maintaining system stability under unexpected conditions.
Review of these experiences shows that combining reinforcement learning techniques with modular architectures can increase agent flexibility. Also creating automatic monitoring mechanisms and error recovery systems are keys to success in developing stable intelligent agents.