{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2609.04298",
  "title": "Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation",
  "summary": "The Harbor Adapters replace bespoke, per-benchmark environment setup and agent integration work with a single ported evaluation infrastructure covering more than 80 benchmarks, and Harbor-Index replaces running full benchmark suites with a curated 82-task subset. The cost is the engineering effort of adopting the Harbor harness and the API spend per evaluation run, plus dependence on adapter fidelity validated by the authors' parity experiments rather than one's own. Failures include adapter or environment bugs silently skewing scores, the curated index drifting from the reader's actual production workload distribution, and pass-rate comparisons becoming stale as new models and harnesses appear.",
  "rationale": "For a small team on a constrained budget, the curated, openly released Harbor-Index and adapters provide a low-cost, low-effort way to benchmark and select agents for production without building evaluation infrastructure from scratch.",
  "technique": "Unified agentic benchmark adapters and curated eval index",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-09-03",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.04298",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 2,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The Harbor Adapters replace bespoke, per-benchmark environment setup and agent integration work with a single ported evaluation infrastructure covering more than 80 benchmarks, and Harbor-Index replaces running full benchmark suites with a curated 82-task subset. The cost is the engineering effort of adopting the Harbor harness and the API spend per evaluation run, plus dependence on adapter fidelity validated by the authors' parity experiments rather than one's own. Failures include adapter or environment bugs silently skewing scores, the curated index drifting from the reader's actual production workload distribution, and pass-rate comparisons becoming stale as new models and harnesses appear.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.04298",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Harbor-Index (82 tasks across 29 benchmarks) preserves the difficulty and breadth of the full adapted suite while being affordable to run; no model-harness configuration exceeds 30% pass rate, with the strongest (GPT-5.5 with Codex) at 28.0%.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.04298",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "selector_failed",
  "core_kind": "",
  "equations_fetched_at": "2026-09-26",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}