{
  "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": "2608.26546",
  "title": "DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows",
  "summary": "DuMateBench replaces clean, capability-isolated agent benchmarks with 200 tasks reconstructed from production user sessions, run in Docker containers with insufficient, unstable, and noisy environment conditions. Adopting it costs engineering time to set up the containerized harness, LLM-as-Judge API calls for scoring, and compute for multi-framework, multi-model evaluation runs. It can fail if the benchmark's tasks do not match the reader's actual production workload, if LLM-judge scoring is inconsistent for their domain, or if teams over-optimize framework choices to the benchmark rather than to their own telemetry.",
  "rationale": "As an evaluation methodology it requires no training or cluster, and the reader should test whether its perturbation-based evaluation predicts failures in their own agent deployment before relying on it for framework or model selection.",
  "technique": "Real-session agent benchmark with environmental perturbations",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-08-27",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2608.26546",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 9,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DuMateBench replaces clean, capability-isolated agent benchmarks with 200 tasks reconstructed from production user sessions, run in Docker containers with insufficient, unstable, and noisy environment conditions. Adopting it costs engineering time to set up the containerized harness, LLM-as-Judge API calls for scoring, and compute for multi-framework, multi-model evaluation runs. It can fail if the benchmark's tasks do not match the reader's actual production workload, if LLM-judge scoring is inconsistent for their domain, or if teams over-optimize framework choices to the benchmark rather than to their own telemetry.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2608.26546",
      "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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      "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_formula",
  "core_kind": "evaluation_protocol",
  "equations_fetched_at": "2026-09-21",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}