The introduction of large language models ignited great retooling and rethinking of the software development models. The ensuing response of software engineering research yielded a massive body of tools and approaches. In this paper, we join the hassle by introducing agentic AI solutions for two tasks. First, we developed a solution for automatic test scenario generation from a detailed requirements description. This approach relies on specialized worker agents forming a star topology with the supervisor agent in the middle. We demonstrate its capabilities on a real-world example. Second, we developed an agentic AI solution for the document retrieval task in the context of software engineering documents. Our solution enables performing various use cases on a body of documents related to the development of a single software, including search, question answering, tracking changes, and large document summarization. In this case, each use case is handled by a dedicated LLM-based agent, which performs all subtasks related to the corresponding use case. We conclude by hinting at the future perspectives of our line of research.

翻译：大型语言模型的引入引发了软件开发模式的重大重构与重新思考。软件工程研究随之涌现出大量工具与方法。本文通过为两项任务引入智能体人工智能解决方案参与这一探索。首先，我们开发了从详细需求描述自动生成测试场景的解决方案。该方法依赖以监督智能体为中心形成星型拓扑结构的专业化工作智能体，并通过实际案例展示了其能力。其次，我们针对软件工程文档中的文档检索任务开发了智能体人工智能解决方案。该方案支持对单个软件开发相关文档集执行多种用例，包括搜索、问答、变更追踪和长文档摘要。在此方案中，每个用例均由专用的基于LLM的智能体处理，该智能体执行与对应用例相关的所有子任务。最后，我们对本研究的未来方向进行了展望。