{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2610.21218",
  "title": "AutoOnboarder: Autonomously Onboarding LLM Agents",
  "summary": "AutoOnboarder replaces manual prompt engineering, model selection, and harness tuning for LLM agents with an autonomous supervisor coding agent that iteratively edits the foundation model choice, prompt, and harness source code using only tens of task examples, with evaluation data isolated on a separate VM. It costs the compute and API spend of running an iterative Onboarder-agent loop on a sandboxed environment, plus the engineering effort to build a robust environment with a structured thought interface, controlled internet access, and a broad model search space. Onboarding outcomes are highly variable and the resulting agents remain imperfect, so a production deployment can inherit silent failures if the small onboarding example set does not represent real workload distribution.",
  "rationale": "The approach runs on API-accessible models and suits constrained infrastructure with a credible cost claim, but the paper's own report of high variance in final performance requires workload-specific validation before production reliance.",
  "technique": "Autonomous agent onboarding via supervisor coding agent",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21218",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "AutoOnboarder replaces manual prompt engineering, model selection, and harness tuning for LLM agents with an autonomous supervisor coding agent that iteratively edits the foundation model choice, prompt, and harness source code using only tens of task examples, with evaluation data isolated on a separate VM. It costs the compute and API spend of running an iterative Onboarder-agent loop on a sandboxed environment, plus the engineering effort to build a robust environment with a structured thought interface, controlled internet access, and a broad model search space. Onboarding outcomes are highly variable and the resulting agents remain imperfect, so a production deployment can inherit silent failures if the small onboarding example set does not represent real workload distribution.",
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      "result": "",
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      "conditions": "",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Autonomously onboarded agents match or exceed a Gemini 3.5 Flash baseline at up to 29x lower cost, though final performance varies greatly across onboarding runs.",
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    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "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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}