{
  "id": 18435907,
  "title": "EnvOpt: Automated Optimization for Agent Environments",
  "url": "https://urgent.news/2026/11/05/envopt-automated-optimization-for-agent-environments",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-05T03:16:52.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.07372v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "An LLM agent's cost and success depend on its model, its harness, and its environment. In addition to the widely studied directions of parameter tuning and harness optimization, we propose the third direction: optimization over stationary environments. Agents are often deployed in rich environments shared by many tasks, such as a company's Slack workspace or a complex collection of data sources,…",
  "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."
}