{
  "id": 18008,
  "url": "https://arxiv.org/abs/2608.09524v1",
  "title": "STAIR: Effective Incident Response Using an End-to-End Agentic Planning Framework",
  "summary": "Incident response planning is critical for restoring compromised software systems after cyberattacks. Common practice relies on expert-driven playbooks that encode fixed response procedures, but these static workflows struggle to adapt to evolving incident states, changing recovery objectives, and execution feedback. Recent LLM-based planners and tool-using agents improve automation, yet they remain unstable in long-horizon response because they lack a unified basis for maintaining incident stat",
  "authors": "Hanlin Jiang, Jionghao Huang, Shaofei Li, Bojia Yu, Peng Jiang, Yuxin Ren et al.",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T12:25:12.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18008",
  "original_url": "https://arxiv.org/abs/2608.09524v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}