{
  "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": "2609.28372",
  "title": "Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer",
  "summary": "The method replaces black-box output-only evaluation of LLM shopping agents with process tracing that places product attributes behind costly tool calls, revealing how agents acquire information before choosing. It costs an instrumented evaluation harness, per-call cost accounting, and multi-model API spend across eight commercial LLMs, plus prompt redesign effort since a specific goal prompt is the main mitigation. It can fail if production prompts remain vague, since agents under acquisition costs then skip diagnostic attributes such as unit-price components and select suboptimal options via heuristic-like shortcuts.",
  "rationale": "The finding that prompt specificity and information architecture, rather than immutable model flaws, govern agent failures is directly actionable for engineers deploying tool-using agents, but the Tool-Lab harness itself must be validated against the reader's own workload before adoption.",
  "technique": "Tool-Lab: cost-instrumented process tracing for shopping agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28372",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2609.28372",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}