{
  "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": "2610.00648",
  "title": "Incident-Arena: Getting agents to the last nine of reliability",
  "summary": "Incident-Arena replaces toy-repository SRE benchmarks and static verifiers with 20 tasks on real open-source applications deployed to ephemeral Kubernetes clusters, using functional verifiers that check systems-level metrics and repair safety. Adopting it costs non-trivial infrastructure (Kubernetes orchestration, fault injection, sustained load generation) and substantial API budget, since single trials average 2.81M tokens. Results can mislead if task selection does not match the reader's incident distribution, and benchmark scores below 64.3% indicate that current agents remain unreliable for unsupervised production incident response regardless of evaluation choice.",
  "rationale": "This is an evaluation benchmark requiring Kubernetes clusters and multi-million-token frontier-model trials, so it exceeds constrained infrastructure while its main practical implication is cautionary: production agents should not yet be trusted with autonomous incident response without human oversight.",
  "technique": "Incident-Arena: Kubernetes-based agentic SRE benchmark",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00648",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Incident-Arena replaces toy-repository SRE benchmarks and static verifiers with 20 tasks on real open-source applications deployed to ephemeral Kubernetes clusters, using functional verifiers that check systems-level metrics and repair safety. Adopting it costs non-trivial infrastructure (Kubernetes orchestration, fault injection, sustained load generation) and substantial API budget, since single trials average 2.81M tokens. Results can mislead if task selection does not match the reader's incident distribution, and benchmark scores below 64.3% indicate that current agents remain unreliable for unsupervised production incident response regardless of evaluation choice.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00648",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Frontier models score below 64.3% across 20 incident-response tasks, with trials averaging 2.81M tokens and 41 turns; this is an evaluation result, not a performance improvement claim.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00648",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "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": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "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": ""
}