Meta Cognitive Monitoring Algorithms For Error Correction I Agentic Reasoning Chains

Abstract

Error propagation in agentic reasoning chains is a major concern for any AI system's self-sufficient decision-making capabilities owing to incomplete information, noisy input signals, or unclear data. It is possible that these errors will reduce the performance of the AI agent in complex situations, emphasizing the importance of monitoring and correcting such issues in real-time. In this work, we propose the use of the Meta-Cognitive Monitoring Algorithm (MCMA). This algorithm aims to detect, assess, and correct errors encountered by an agentic reasoning chain. This research proposes a new approach in the field with the primary objective of increasing the accuracy, effectiveness, and explainability of the reasoning process by employing the meta-cognitive abilities of AI systems. The algorithm consists of three main components, namely, error detection, self-assessment, and correction strategy. The performance indicators used were Accuracy (%), Correction Rate (%), Computational Efficiency (ms), and Confidence Calibration (%). The baseline studies included conventional reasoning methods and rule-based correction techniques. It is observed from the experimental studies that the MCMA model can be able to deliver an accuracy of 92.4%, a correction rate of 89.7%, and confidence calibration of 91.5%. MCMA has surpassed the accuracy rates of the conventional reasoning approach with 72.5% accuracy and 40.2% correction rate as well as the rule-based correction technique with 81.8% accuracy and 63.5% correction rate. The computational efficiency was found to be 22 ms per reasoning chain. Further research will focus on real-time adaptation, hierarchical reasoning, and compatibility with hybrid neural-symbolic systems to perform sophisticated AI operations.