Authors - Aditya Kasture, Supriya Narad Abstract - The pace of change of the software development industry is unprecedented as the introduction of AI code generation tools has not only doubled the productivity of developers by up to 55 percent but also introduced the industry with a new problem of exponential growth in the complexity of the code and technical debt. The former techniques of code review are monotonous, infrequent and time consuming. Such an approach cannot validate the mammoth amounts of gains that are evident in an AI-oriented development cycle. The structure, performance, and service of AI-Accelerated Code Review (AACR) Platforms, which we discuss in this paper, would be the last mile of quality control that would be the solution to this so-called paradoxical situation of such engineering productivity. We propose an AACR system, which is built on a Multi-Agent Architecture with Large Language Models (LLM) to accomplish contextual and reasoning problems, custom machine learning (ML) models to evaluate security and performance, and a code graph analysis to obtain a good composition of the codebase. We conclude that median code review time is an option to decrease by 40-60 per- cent with the AACR platforms. Besides, the accuracy of the detection of the defects can also rise in comparison with the old method of analyzing and reviewing of the data manually. The article relies on the primary argument presented in the description above and the debates concerning the unlawful use of AI generated data and the in- creased use of AI.
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