{
  "annotation_id": "dceddb3a-aac2-4b40-8f2f-584b46a7288b",
  "date_created": "2026-02-17T05:53:23.473000Z",
  "date_modified": "2026-02-17T05:53:23.473000Z",
  "file_hash": "2001830424eeaa1c5fee5cfbbf76f23013ab85c61251424f22a21c3d853bd2bc",
  "private": false,
  "record": {
    "abstract": "Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems, examining their applications across the Software Development Life Cycle (SDLC), from requirements engineering and code generation to static code checking, testing, and debugging. We delve into a wide range of topics such as language model selection, SE evaluation benchmarks, state-of-the-art agentic frameworks and communication protocols. Furthermore, we identify key challenges and outline future research opportunities, with a focus on multi-agent orchestration, human-agent coordination, computational cost optimization, and effective data collection. This work aims to provide researchers and practitioners with valuable insights into the current forefront landscape of agentic systems within the software engineering domain.",
    "arxiv_id": "2601.09822",
    "authors": [
      "Yongjian Tang",
      "Thomas Runkler"
    ],
    "categories": [
      "cs.SE",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities",
    "url": "https://arxiv.org/abs/2601.09822",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8411d1b8-b35a-4a41-9156-32e306062075",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}