{
  "id": 12526384,
  "title": "Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?",
  "url": "https://urgent.news/2026/10/06/agent-in-a-bottle-can-llm-agents-turn-their-capabilities-into-cheap",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-06T17:57:19.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.08775v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive. Can LLM agents autonomously create cheaper solutions for such workloads? We call this ability \"bottling\": the ability to turn general capabilities into task-specific solutions that balance answer quality and amortised cost. We introduce BOTTLED,…",
  "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."
}