{
  "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": "2609.37864",
  "title": "AgentBug-Smith: Automatically Reproducing Real-World Harness Bugs in Agentic Systems",
  "summary": "The method replaces manual construction of executable harness-bug benchmarks (hundreds of human hours) with automated discovery and reproduction of real bugs from open-source agentic systems, yielding the 200-bug Live-Harness-Bench plus a distilled repair-skill knowledge base. Cost is modest for the reader: the benchmark and skills can be consumed via API-based agents, but reproducing bugs requires setting up executable environments for external agentic codebases, which adds integration and compute overhead. Failure modes include benchmark contamination as the same open-source bugs enter training corpora, reproduction scripts that break as upstream repositories evolve, and distilled repair skills that may not transfer to the reader's specific harness or stack.",
  "rationale": "The reader evaluating or improving production agent systems can cheaply adopt the released benchmark and repair skills to test agent robustness, but should validate that the distilled skills generalize to their own harness before relying on the reported gains.",
  "technique": "Automated harness-bug reproduction and live benchmark",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37864",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manual construction of executable harness-bug benchmarks (hundreds of human hours) with automated discovery and reproduction of real bugs from open-source agentic systems, yielding the 200-bug Live-Harness-Bench plus a distilled repair-skill knowledge base. Cost is modest for the reader: the benchmark and skills can be consumed via API-based agents, but reproducing bugs requires setting up executable environments for external agentic codebases, which adds integration and compute overhead. Failure modes include benchmark contamination as the same open-source bugs enter training corpora, reproduction scripts that break as upstream repositories evolve, and distilled repair skills that may not transfer to the reader's specific harness or stack.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37864",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "AgentBug-Smith reports 10.67%-27.56% higher bug-reproduction success rates than general-software reproduction techniques, and skills distilled from Live-Harness-Bench raise harness-bug repair rates of software agents by 6.32 percentage points; neither is a multiplicative factor.",
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      "source_url": "https://arxiv.org/abs/2609.37864",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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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": "",
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}