{
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  "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": "2610.30769",
  "title": "LLM Multi-Agent Systems Are Vulnerable to Herding and Runaway Cascades",
  "summary": "This work replaces the assumption that aggregating multiple LLM agents reliably improves collective accuracy by demonstrating that social influence creates herding vulnerabilities analogous to human groups but with reduced self-correction. Implementing safeguards requires architectural changes such as limiting visibility into peer choices, restructuring information flow, or introducing diversity-preserving mechanisms, which adds system complexity and may reduce the coordination benefits of multi-agent setups. The primary failure mode is runaway cascades where agents converge on suboptimal outcomes amplified by feedback loops, particularly when agents can observe cumulative recommendation counts.",
  "rationale": "The reader operating multi-agent systems in production should validate whether their specific orchestration topology exposes agents to peer influence in ways that trigger herding, as the paper demonstrates this failure mode is severe in LLM populations and lacks the self-correction mechanisms present in human groups.",
  "technique": "Multi-agent herding and cascade vulnerability analysis",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30769",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 136,
  "citations": 0,
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