{
  "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": "2610.22350",
  "title": "FDE-Gym: The Last Mile of AI Delivery",
  "summary": "FDE-Gym replaces well-specified issue-ticket agent benchmarks with 95 ambiguous delivery tasks in simulated customer environments, scored by improvement in the customer's own business metric with penalties for stakeholder interactions. Adoption as an evaluation harness costs integration effort against its simulated environments and platform access, and the interaction penalties may not match a given deployment's cost model. It can fail as a selection tool if its scripted stakeholders and sector-specific scenarios do not transfer to the reader's actual customer workflows, leading to mis-ranked model or orchestration choices.",
  "rationale": "This is a diagnostic benchmark rather than a deployable technique, but its evidence that frontier models fail at discovering unstated constraints is directly actionable for scoping production agent deployments.",
  "technique": "FDE-Gym benchmark for end-to-end agent delivery",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.22350",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FDE-Gym replaces well-specified issue-ticket agent benchmarks with 95 ambiguous delivery tasks in simulated customer environments, scored by improvement in the customer's own business metric with penalties for stakeholder interactions. Adoption as an evaluation harness costs integration effort against its simulated environments and platform access, and the interaction penalties may not match a given deployment's cost model. It can fail as a selection tool if its scripted stakeholders and sector-specific scenarios do not transfer to the reader's actual customer workflows, leading to mis-ranked model or orchestration choices.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.22350",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Best of 13 configurations achieves a mean score of 0.56 in the realistic setting and fully resolves only 14 of 95 tasks; discovering unstated constraints is the primary bottleneck, and even with all key facts obtained, only 10 of 23 tasks reach normalized uplift >= 0.8.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.22350",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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      "dimension": "compute",
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      "source_url": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "limitation": "",
      "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": "",
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}