{
  "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": "2608.05013",
  "title": "OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents",
  "summary": "OneDayAgent replaces ad hoc single-prompt or manually scripted agent loops with a managed execution process that decomposes open-ended requests into bounded subtasks, maintains execution memory under context pressure, and verifies and repairs the final deliverable. It costs additional orchestration complexity and extra backend calls for decomposition, memory management, and verification, which increases latency and API spend per task relative to a single-pass agent. It can fail if the backend model executes the prescribed workflow poorly, if decomposition produces incorrect subtask boundaries, or if the verifier accepts a flawed deliverable, and the reported score is specific to one benchmark whose tasks may not match the reader's production workload.",
  "rationale": "The harness is implementable with third-party APIs and no training, but the quality claim is benchmark-specific with no reported baseline, so adoption should follow validation on the reader's own tasks.",
  "technique": "Long-horizon agent execution harness",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-08-04",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2608.05013",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 43,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "OneDayAgent replaces ad hoc single-prompt or manually scripted agent loops with a managed execution process that decomposes open-ended requests into bounded subtasks, maintains execution memory under context pressure, and verifies and repairs the final deliverable. It costs additional orchestration complexity and extra backend calls for decomposition, memory management, and verification, which increases latency and API spend per task relative to a single-pass agent. It can fail if the backend model executes the prescribed workflow poorly, if decomposition produces incorrect subtask boundaries, or if the verifier accepts a flawed deliverable, and the reported score is specific to one benchmark whose tasks may not match the reader's production workload.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2608.05013",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Achieves state-of-the-art overall score of 0.821 on AgentIF-OneDay (104 tasks) with a GLM-5.2 backend, and runs across five backend LLMs from three families without tuning; no baseline score is given, so no multiplicative gain is stated.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2608.05013",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "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": "no_equations",
  "core_kind": "",
  "equations_fetched_at": "2026-09-16",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}