Explainable Multi-Agent AI Systems for Intelligent Software Engineering and Business Automation

<title>Abstract</title> <p>Generative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) are revolutionizing software programming and enterprise business automation in the 4th industrial revolution era. Current multi-agent systems, however, are constrained in being able to collaborate, in understanding how to reach consensus, in their knowledge sharing and in their ability to adapt to changes and therefore are not suitable for certain industrial critical applications. To address the above challenges, this paper introduces a cognitive swarm-based multi-agent framework with collaborative reasoning, collective cognitive memory, explainable decision intelligence, digital twin simulation and autonomous governance, which are integrated into an explainable cognitive swarm control framework XCognitive SwarmNet. Proposed framework mathematically models agent collaboration, trust, explainability, Knowledge sharing and Optimization of the flow of business tasks to support the complete Software Development Lifecycle and Intelligent Business process Automation. By using the powerful and representative software engineering and business process datasets, some experimental assessments are performed and the proposed mechanism is compared to six state-of-the-art multi-agent approaches. The proposed framework completes the task with 98.1% accuracy, gains in 22.8% in the software quality, achieves 97.5% in explainability fidelity, optimizes the workflow by 95.9% and optimizes the efficiency of agent collaboration by 94.7% with a 27.4% reduction in execution time. Overall, the results showed that XCognitive SwarmNet is a suitable solution to achieve transparent, adaptive and trustworthy AI-driven digital transformation for Industry 4.0.</p>