As software systems continue to grow in complexity and scale, traditional software engineering approaches face increasing challenges in design, implementation, maintenance, and evolution. The emergence of powerful Generative AI technologies, particularly Large Language Models (LLMs), has created unprecedented opportunities to re-imagine how software is developed and maintained. Simultaneously, multi-agent systems offer established frameworks for distributed intelligence, collaboration, and autonomous problem-solving that can effectively address many of the inherent challenges in modern software engineering. The convergence of these two paradigms (i.e., generative AI capabilities and multi-agent architectures) presents a promising new frontier for automated software engineering.

In such a context, this workshop offers a unique platform to bring together leading researchers, industry practitioners, and visionary experts to explore the integration of Multi-Agent Systems (MAS), Generative Artificial INtelligence (GAIN), and automated software engineering. We aim to foster interdisciplinary discussions and spark innovative ideas that push the boundaries of current technology. In an era where complex software systems demand more adaptive, resilient, and autonomous solutions, multi-agent architecture provides a powerful paradigm for distributed problem-solving and collaborative decision-making. At the same time, breakthroughs in generative artificial intelligence, most notably with LLMs, are enabling unprecedented capabilities in automating the design, development, and maintenance of software systems. These advances not only streamline coding and testing processes but also open new avenues for creative systems and software engineering.

The MAS-GAIN 2025 provides a forum for researchers and practitioners to propose and discuss these advancements, addressing both theoretical foundations and practical implementations. Participants are encouraged to share research findings, case studies, and emerging trends that demonstrate how the synergy between MAS, GAIN (including LLM-driven approaches), and automated software engineering can lead to smarter, more efficient, and scalable software engineering practices.

By bridging these domains, the workshop aims to lay the groundwork for next-generation software solutions that are both intelligent and autonomous.

Topics of interest include, but are not limited to, the following:

• MAS for software engineering powered by generative AI, orchestration, and novel frameworks for MAS coordination
• Application of MAS for code synthesis, system modeling, and overall software engineering.
• Integration of heterogeneous models in multi-agent platforms
• Development of visual user interfaces and low/no-code support
• Optimization strategies concerning agent energy consumption and resource usage
• Research on local deployment and dockerization of MAS
• Recommendations of agents based on tasks or characteristics
• Integration of agent frameworks with development environments and software engineering tools
• Ethical considerations in AI-driven automated software engineering
• Verification and validation of AI-generated code and artifacts
• Human-agent collaboration
• Agent-based approaches to software maintenance and evolution
• Industrial applications, case studies, and empirical evaluations of MAS

Please note that surveys, (systematic) literature reviews, and mapping studies are out of the scope of this workshop and will be desk-rejected.