{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.19843",
  "title": "LAGO: Language Agents with Reusable Growing Options",
  "summary": "LAGO replaces a single monolithic tool-use loop, in which one agent must decompose, execute, and verify within one trajectory, with a meta-agent that retrieves or spawns specialized sub-agents stored as reusable options in a persistent library that grows across tasks without weight updates. The cost is orchestration complexity plus additional inference calls for meta-agent dispatch, verification, and admission of new options, since a frozen model is used in both roles and no training is required. It can fail when decomposition quality is poor, when the library accumulates mislabeled or non-transferable options, or when gains do not replicate across models and task distributions, as the ablation shows component contributions are task- and model-dependent.",
  "rationale": "The method runs on frozen models with no training and is accessible via quantized weights or APIs, but the improvement over the strongest baseline is only about 3 points on a matched subset, so the added orchestration must be validated against the reader's own task stream before production adoption.",
  "technique": "Reusable library of agent options for long-horizon tasks",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19843",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
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