Agentic AI for Early Bug Detection and Software Quality Assurance: A Multi-Agent Collaborative Framework

The modern software systems requires a proactive, scalable, and continuously adaptive quality assurance techniques to maintain the pace with iterative Agile workflows and CI/CD pipelines. Conventional static and dynamic analysis tools remain largely reactive, isolated, and that might lead to a high false positive rates (FPR). To address all such challenges, the current study has proposed an Agentic Artificial Intelligence (AAI) framework that is collaborative, multi-agent architecture designed for early software defect prediction (SDP) and continuous quality monitoring. The framework orchestrates specialized, autonomous agents dedicated to static-semantic code analysis, defect prediction, automated test generation, patch synthesis, and quality assessment. Code representations are constructed using Graph Attention Networks (GAT) on Abstract Syntax Trees (AST) combined with CodeBERT semantic embeddings and BiLSTM temporal evolution modeling. Furthermore, a reinforcement learning (RL) feedback mechanism dynamically adapts agent policies using sprint-level performance signals. Evaluated across standard PROMISE (JM1, KC1, PC1) and Defects4J benchmark datasets, the proposed framework achieves an average accuracy of 0.87, a recall of 0.87, and reduces the false positive rate to 0.12–0.15. Additionally, automated test generation improves code coverage up to 82% while lowering the mean time to bug detection (MTTD) to 10.5–12.1 hours. These results demonstrate that multi-agent collaborative reasoning significantly enhances early defect localization and provides continuous quality assurance in modern software engineering.