{
  "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": "2610.26394",
  "title": "When Coding Agents Outpace Review: Evaluating PRD-Driven Development in Mature Enterprise Repositories",
  "summary": "PACE replaces ad hoc or issue-resolution-style evaluation of coding agents with evidence-based scoring of full PRD fulfillment against 750 expert-audited Code Points in mature repositories. It costs evaluation engineering effort: tasks must be adapted or the benchmark adopted as-is, expert-style auditing is labor-intensive, and running 43 enterprise tasks against candidate model-scaffold configurations consumes API budget. It can fail as a selection signal because even the best configuration achieves only a 14.0% complete-delivery rate, so benchmark ranking may not translate into dependable autonomous delivery on the reader's own repositories.",
  "rationale": "The benchmark is directly relevant to production adoption decisions, but its low complete-delivery rates mean the reader should use it to validate agent and harness choices on their own workload rather than adopt autonomous PRD-driven development outright.",
  "technique": "PRD-fulfillment benchmark for enterprise coding agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26394",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 1,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PACE replaces ad hoc or issue-resolution-style evaluation of coding agents with evidence-based scoring of full PRD fulfillment against 750 expert-audited Code Points in mature repositories. It costs evaluation engineering effort: tasks must be adapted or the benchmark adopted as-is, expert-style auditing is labor-intensive, and running 43 enterprise tasks against candidate model-scaffold configurations consumes API budget. It can fail as a selection signal because even the best configuration achieves only a 14.0% complete-delivery rate, so benchmark ranking may not translate into dependable autonomous delivery on the reader's own repositories.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26394",
      "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",
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      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "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": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}