{
  "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": "2610.13361",
  "title": "AgentSWE: Can Coding Agents Build the Agent You Actually Want?",
  "summary": "AgentSWE replaces ad hoc acceptance of agent-generated agent code with commission-style evaluation: a frozen submission is tested on held-out cases graded against requirement-derived rubrics under pass/fail gates, rather than being judged by its behavior on the illustrative cases the builder saw. The cost is evaluation engineering effort: constructing held-out cases and rubrics per requirement, plus the API or GPU spend to run frozen submissions, and it measures rather than improves delivered quality. It can fail to transfer if the reader's tasks are narrower than the benchmark's 25 commissions, and rubric-based grading inherits the reliability limits of whatever grader is used.",
  "rationale": "The paper contributes a benchmark and failure analysis rather than a deployable method, but its central finding—that coding agents systematically overfit visible cases and claim unsupported completion—directly justifies adopting held-out, rubric-gated acceptance testing as a low-cost validation practice before shipping agent-built agents.",
  "technique": "Held-out rubric benchmark for agent-building coding agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13361",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 12,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "AgentSWE replaces ad hoc acceptance of agent-generated agent code with commission-style evaluation: a frozen submission is tested on held-out cases graded against requirement-derived rubrics under pass/fail gates, rather than being judged by its behavior on the illustrative cases the builder saw. The cost is evaluation engineering effort: constructing held-out cases and rubrics per requirement, plus the API or GPU spend to run frozen submissions, and it measures rather than improves delivered quality. It can fail to transfer if the reader's tasks are narrower than the benchmark's 25 commissions, and rubric-based grading inherits the reliability limits of whatever grader is used.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.13361",
      "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",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}