{
  "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": "2610.22316",
  "title": "Learning to Steer LLM Agents and Multi-Agent Systems",
  "summary": "The method replaces ad hoc prompt-based monitoring or unmonitored execution of LLM agents with a separate trained supervisor that detects goal drift and intervenes via a low-cost fast path (when to intervene) and a high-cost slow path (how to intervene). It costs an RL training pipeline (pairwise ranking plus GRPO), an additional model in the inference loop, and added latency and token spend per execution step, though intervention frequency is controllable. It can fail through over- or under-intervention on workloads unlike the training distribution, drift-assessment errors that interrupt correct trajectories, and dependence on GRPO training infrastructure that may exceed the reader's budget.",
  "rationale": "The abstract reports improvements and out-of-domain generalization but no quantified results, and the RL training requirement makes adoption conditional on validating gains with the reader's specific agent workloads and budget.",
  "technique": "RL-trained steering agent for goal-drift correction",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.22316",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
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