{
  "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": "2609.17698",
  "title": "A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents",
  "summary": "This empirical study replaces ad hoc, feature-level testing of LLM agents with a systematic QA taxonomy covering execution surfaces, safeguards, and end-to-end validation of tool use, untrusted inputs, and persistent state. Adopting its guidance costs engineering time to write boundary, adversarial, and multi-step tool-failure tests and to audit safeguard consistency across equivalent execution routes, but requires no additional GPU or model infrastructure. If adopted superficially, the checklist can produce false assurance: coverage of listed risk scenarios does not guarantee detection of novel failure modes, and findings from open-source projects may not transfer to proprietary production agents.",
  "rationale": "The study's QA taxonomy and identified gaps are directly actionable for a small team shipping agents, requiring only test-writing effort rather than compute or model training.",
  "technique": "End-to-end QA checklist for LLM agent workflows",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17698",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This empirical study replaces ad hoc, feature-level testing of LLM agents with a systematic QA taxonomy covering execution surfaces, safeguards, and end-to-end validation of tool use, untrusted inputs, and persistent state. Adopting its guidance costs engineering time to write boundary, adversarial, and multi-step tool-failure tests and to audit safeguard consistency across equivalent execution routes, but requires no additional GPU or model infrastructure. If adopted superficially, the checklist can produce false assurance: coverage of listed risk scenarios does not guarantee detection of novel failure modes, and findings from open-source projects may not transfer to proprietary production agents.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.17698",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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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",
      "finding": "",
      "interpretation": "",
      "limitation": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}