{
  "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": "2610.31003",
  "title": "Agri-SCHOLAR: Simulation-Grounded In-Season Rolling Advisories for Agricultural Crops Using LLM-Bounded Optimization",
  "summary": "It replaces expert-driven, static packages of practice with rolling in-season advisories in which LLM agents bound actionable agronomic parameters and an optimizer searches them against a crop simulator (APSIM) acting as an evaluation oracle. The cost is substantial integration complexity: licenses and calibration for a crop simulator, per-farm soil and weather data pipelines, multi-agent LLM API calls at every advisory interval, and optimizer compute per rollout. It can fail where simulator calibration drifts from local field conditions, where LLM-proposed bounds are infeasible or unsound, or where weather analogs are unrepresentative, and the reported gains are validated only for maize at two sites.",
  "rationale": "The pattern is architecturally interesting but depends on domain simulators, agronomic data, and validation outside the reader's ML production scope, so it warrants monitoring rather than adoption.",
  "technique": "LLM-bounded simulation optimization for advisories",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.31003",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
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      "dimension": "compatibility",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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}