{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2610.28380",
  "title": "Object-Oriented Harness: Evolving Agent Harnesses through Object-Oriented Design",
  "summary": "OO-Harness replaces a single global agent harness (fixed prompts, tools, and control flow) with an evolved hierarchy of specialized harness subclasses selected per task by a router trained on execution records. The cost is an offline evolution phase in which an evolution agent runs tasks and generates/refines harness code, adding API spend and engineering complexity, though test-time routing itself is lightweight. It can fail when the router misclassifies tasks, when evolved harnesses overfit the training task distribution and generalize poorly to new task types, or when the evolution budget is too small to discover useful specializations.",
  "rationale": "The method requires no GPU or model training and works through API-driven evolution and routing, but the claimed gains are benchmark-specific and the reader must validate that the evolution cost and routing reliability pay off on their own task mix before production adoption.",
  "technique": "Evolving object-oriented agent harness classes",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28380",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "OO-Harness replaces a single global agent harness (fixed prompts, tools, and control flow) with an evolved hierarchy of specialized harness subclasses selected per task by a router trained on execution records. The cost is an offline evolution phase in which an evolution agent runs tasks and generates/refines harness code, adding API spend and engineering complexity, though test-time routing itself is lightweight. It can fail when the router misclassifies tasks, when evolved harnesses overfit the training task distribution and generalize poorly to new task types, or when the evolution budget is too small to discover useful specializations.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28380",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Best performance across three long-horizon benchmarks (business workflows, workspace tasks, tool use), with absolute improvement of up to 15% over the base harness.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28380",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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    },
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      "dimension": "compute",
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      "finding": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "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": ""
}