{
  "id": 10615507,
  "title": "TokenCast: Forecasting Token Consumption During LLM Agent Execution",
  "url": "https://urgent.news/2026/09/28/tokencast-forecasting-token-consumption-during-llm-agent-execution",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-28T17:59:09.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.35760v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses its next steps based on tool feedback and intermediate results, while the growing context steadily inflates the input size of every subsequent call. The total consumption of a task is therefore hard to predict before execution and the prediction…",
  "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."
}