{
  "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": "2609.26781",
  "title": "Agensh: Scaling Organizational Intelligence to 1,024 Agents",
  "summary": "Agensh replaces the central-orchestrator pattern in multi-agent harnesses with a decentralized loop in which workers claim sub-tasks, act, and merge progress asynchronously through a shared workspace, message interface, and shared context. The cost is large-scale API consumption: demonstrated gains rely on up to 1,024 concurrent GPT-5.6-sol (high) workers, which is infeasible under a limited cloud budget and yields only modest absolute test-pass improvements. Failure modes include uncoordinated or duplicated work, conflicting merges, and runaway token spend, since self-organization offers no central control over task allocation or budget.",
  "rationale": "The validated gains require hundreds to over a thousand concurrent frontier-model agents, a cost profile incompatible with one 24 GB GPU or a limited API budget, so the reader should track the approach rather than deploy it.",
  "technique": "Decentralized self-organized multi-agent harness",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26781",
  "independent_implementations": 5,
  "total_implementations": 5,
  "stars": 11,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Agensh replaces the central-orchestrator pattern in multi-agent harnesses with a decentralized loop in which workers claim sub-tasks, act, and merge progress asynchronously through a shared workspace, message interface, and shared context. The cost is large-scale API consumption: demonstrated gains rely on up to 1,024 concurrent GPT-5.6-sol (high) workers, which is infeasible under a limited cloud budget and yields only modest absolute test-pass improvements. Failure modes include uncoordinated or duplicated work, conflicting merges, and runaway token spend, since self-organization offers no central control over task allocation or budget.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.26781",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Scaling from 1 to 128 agents raises mean final test-pass rate on the five hardest ProgramBench tasks from 19.31% to 28.78% (about 49% relative); on pandoc, 1 to 1,024 agents raises it from 33.89% to 55.06%.",
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      "source_url": "https://arxiv.org/abs/2609.26781",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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  "equations_source_sha256": "",
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}