{
  "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": "2609.40303",
  "title": "How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?",
  "summary": "This approach replaces elaborate multi-agent orchestrators, retrieval subagents, and hand-crafted scaffolding for autonomous ML engineering with a single coding-agent session giving the LLM direct read, write, and bash access to the execution environment. It costs far less engineering effort and orchestration complexity, and reduces per-run token overhead from inter-agent communication, while relying on a frontier API backbone rather than local infrastructure. It can fail when tasks genuinely require parallel exploration or specialization beyond one session, when the available backbone is weaker than the frontier models used in the study, or when benchmark results do not transfer to the reader's production workloads.",
  "rationale": "The paper's ablations indicate harness complexity adds cost without measurable gains under equal budgets, so a constrained team should adopt the simple baseline and direct saved effort toward backbone selection and task-specific validation.",
  "technique": "Minimal-harness single-session coding agent",
  "family": "orquestracao_multiagente",
  "recommendation": "adotar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.40303",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
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      "claim_id": "claim-01",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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