{
  "id": 11077212,
  "title": "DAGent: Evaluate-then-Grow Planning for Deep Research Agents",
  "url": "https://urgent.news/2026/09/30/dagent-evaluate-then-grow-planning-for-deep-research-agents",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-30T07:19:01.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.39154v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emerge. Directed acyclic graph (DAG)-based multi-agent systems suit this setting because they support parallel execution and isolate each sub-task within a focused dependency context. Yet existing DAG-based agents instantiate a task-level plan before…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}