The MCP Dual Cycle Reasoner is a metacognitive enhancement tool designed for autonomous AI agents. Through a dual-cycle framework (sentinel monitoring and arbiter management), it enables anomaly detection and experience learning to enhance the self-awareness and reliability of agents.

An intelligent e-commerce dialogue agent system based on reinforcement learning, integrating ontology reasoning, business toolchains, dialogue memory, and a Gradio interface. It realizes closed-loop learning from data to training and then to deployment through the Stable Baselines3 PPO algorithm, and can autonomously optimize the decision-making strategy of the shopping assistant.