{
  "id": 14618116,
  "title": "LLM agents: budget for each tool",
  "url": "https://urgent.news/2026/10/15/agentes-llm-presupuesto-para-cada-herramienta",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-15T05:23:04.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/silviutech/agentes-llm-presupuesto-para-cada-herramienta-1f8b"
  },
  "original_language": "es",
  "account": "An agent's speed in a demo can differ from its performance in production, where tasks may involve multiple API calls, verification emails, and retries. To manage this, a budget can be set for each task, outlining a deadline, maximum tool calls, and estimated cost. This budget serves as a contract between the agent and the tools it uses, allowing the agent to reason about the task without consuming unlimited resources. A sample contract is provided, detailing the budget and timeouts for specific tools, and a data model is proposed to track the agent's progress and resource usage.",
  "summary": "Un agente LLM puede parecer rápido en una demo porque solo llama una herramienta y termina. En producción, la misma tarea puede consultar un API, esperar un correo de verificación, reintentar y pedir una segunda validación. El problema ya no es solamente qué herramienta elegir: es cuánto presupuesto puede consumir cada llamada. En mis automatizaciones prefiero tratar ese presupuesto como un…",
  "key_points": [],
  "editors_take": "Implementing granular limits for every tool call transforms unpredictable AI agents into controlled production systems by preventing runaway costs and infinite retry loops caused by external latency or errors.",
  "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."
}