{
  "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": "2610.21746",
  "title": "The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance",
  "summary": "The method replaces purely analytical game-theoretic analysis of delegation with 30-agent LLM simulations used to probe emergent institutional structures. It costs API calls across matched simulation pairs plus a repeated-game orchestration harness, and provides no efficiency, latency, or quality improvement for production inference workloads. Its findings are descriptive rather than prescriptive: observed cycles of concentration, exploitation, and exit depend on prompt design and model choice, and the cross-model behavioral difference (Sol versus Luna) may not replicate across other models or scales.",
  "rationale": "This is a social-science study that uses LLM agents as experimental subjects rather than a production technique, so it offers no adoptable method, cost saving, or quality gain for a constrained-infrastructure engineering team.",
  "technique": "LLM-agent simulation of endogenous delegation and polycentric governance",
  "family": "orquestracao_multiagente",
  "recommendation": "nao_aplica",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21746",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "dimension": "reproducibility",
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  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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}