{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task 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": "Correct completed actions, invalid calls, retries, latency, and cost per completed 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": "2609.40169",
  "title": "Learning from Research: Toward Lifelong Agent Harness Evolution",
  "summary": "Replaces manual, reactive prompt-and-harness tuning of tool use, memory, and execution scaffolding with a meta coding agent that proposes and evaluates harness modifications drawn from topic-modeled research literature. Costs include running a capable meta agent plus repeated evaluation of strategy combinations, which adds API spend, implementation complexity, and benchmark engineering without changing the base model. Can fail when retrieved strategies do not transfer to the production task distribution, when combinatorial evaluation overfits public benchmarks, or when literature-driven changes introduce regressions the evaluation harness does not detect.",
  "rationale": "The approach is promising for fixed-model agent improvement but requires a full evolution-and-evaluation pipeline and benchmark infrastructure that a small team should validate at small scale before adopting in production.",
  "technique": "Research-guided automated agent harness evolution",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.40169",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
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      "claim_id": "claim-01",
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      "statement": "Raises Qwen3.5-27B task goal completion from 49.6% to 63.6% on AppWorld Challenge and GPT-5.4-mini pass@1 from 72.7% to 81.9% on Tau2-Bench Telecom.",
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  "exposure_map": [
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      "dimension": "latency",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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  "risks": [],
  "minimum_test": [],
  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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  "equations_source_sha256": "",
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}