{
  "id": 15768,
  "url": "https://arxiv.org/abs/2607.29405v1",
  "title": "Beyond Component Testing: Validating Agentic AI Systems",
  "summary": "Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation. This behavior stretches validation practice beyond component testing and one-shot input--output evaluation, because acceptable system behavior now depends on how decisions unfold over time and under changing environmental conditions. This survey synthesizes 257 papers spanning agent evaluation, software assurance, cyber-physical systems, runtime monitoring, and regulatory",
  "authors": "Fabio Orazio Mirto, Luca D'Agati, Giuseppe Tricomi, Stefano Silvestri, Francesco Longo, Antonio Puliafito, Giovanni Merlino",
  "category": "research",
  "topics": "regulation,military-security,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-31T13:24:34.000Z",
  "fetched_at": "2026-08-03T05:10:47.622Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/15768",
  "original_url": "https://arxiv.org/abs/2607.29405v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}