Adaptive Model Context Protocols for Trustworthy and Secure Agentic AI Systems

Abstract

The Adaptive Model Context Protocols Framework, the proposed AMCP Framework, offers a reliable, safe, and flexible system on which agentic AI can perform based on multi-context settings. The model offers context sensitivity and operational integrity wherein context-aware meta-learning and Bayesian trust estimation, zero-trust security, and federated adversarial defense management are implemented. The performance of the model has been greatly improved as indicated by the results gained after the tests run after 20 training epochs. The accuracy was in the range of 0.71-0.93, the precision was 0.69-0.91 and the recall was 0.66-0.89. The fact that the F1-score consistently converged at 0.90 confirmed dynamic learning, which is balanced understanding of context and policy adaptation and adaptation efficiency of 0.78 to 0.92. In general, they show that the AMCP framework can ensure that the AI responds in a safe and sensitive way depending on its surroundings. It is also able to support clear-cut decision making which is rather essential to autonomous and ethical stable AI systems.