{
  "confidence": 0.9,
  "rationale": [
    "P2V-14 results show pipeline and strong direct achieve comparable creative quality (8.64 vs 8.76).",
    "Pipeline provides superior engineering traceability, extraction, and review stability, aligning with program-first goal.",
    "Direct minimal failure indicates prompt underspecification risk, which pipeline's structured extraction mitigates.",
    "Strong direct lane's director-workbench constraints can be integrated into pipeline without sacrificing traceability.",
    "Advisory-only boundary and control-plane compaction are essential to maintain project discipline."
  ],
  "overall_recommendation": "Adopt pipeline-first strategy with reinforced constraints from strong direct lane; continue control-plane compaction; formalize AI consultant advisory protocol.",
  "recommended_strategy": "pipeline_first",
  "pipeline_changes": [
    "Integrate director-workbench constraints (C/P2V-12/13) into pipeline prompt templates to raise creative quality.",
    "Add prompt-readiness validation step to detect underspecification before packet generation.",
    "Implement per-chapter extraction quality scoring with automatic fallback to strong direct lane when pipeline quality < threshold.",
    "Optimize extraction steps to reduce token usage while preserving traceability metadata.",
    "Maintain pipeline as primary production lane; keep strong direct as optional alternative / quality gate."
  ],
  "control_plane_advice": [
    "Enforce minimal_md_policy: no new markdown unless artifact-backed contract, retrospective, operator decision record, report, or handoff.",
    "Compact workflow.json by offloading static policies to versioned configs; keep machine-readable registry lean.",
    "Cap execution_state.json and phase_acceptance.json at 50KB; archive older states to cold storage.",
    "Preserve advisory-only boundary: no automated operator acceptance; all changes must go through operator decision record."
  ],
  "deepseek_consultant_operating_model": [
    "All output must be JSON with explicit 'advisory' flag and must not alter project state.",
    "Each recommendation must include rationale, risks, and a 'requires_operator_decision' boolean.",
    "Consultant cannot write to execution_state.json, workflow.json, or any project file; only produce messages.",
    "Operator must log acceptance/rejection in an operator decision record before any advised action takes effect.",
    "Consultant must self-assess confidence and declare boundary of competence; if uncertain, recommend human judgment."
  ],
  "next_decision_gate": [
    "Human operator reviews P2V-14 gate: decide whether pipeline or hybrid strategy proceeds to production.",
    "Operator validates P2V-14 results against acceptance criteria (creative quality, engineering stability).",
    "Operator inspects active sidecar (P计划 V2) and determines if generated output meets redesign intent.",
    "Operator updates execution_state.json.next_entrypoint after decision, transitioning to next phase (e.g., pipeline refinement, scale batch, or retrospective)."
  ],
  "risks": [
    {
      "risk": "Over-constraining pipeline reduces creative flexibility",
      "severity": "medium",
      "mitigation": "Keep strong direct lane as optional alternative; allow per-chapter hybrid selection."
    },
    {
      "risk": "Consultant advice misinterpreted as operator acceptance",
      "severity": "high",
      "mitigation": "Require explicit operator decision records; all advice logged and reviewed before action."
    },
    {
      "risk": "Pipeline engineering complexity grows without compaction",
      "severity": "medium",
      "mitigation": "Enforce size limits on control-plane files; archive old states regularly."
    },
    {
      "risk": "Scaling batch introduces quality drift",
      "severity": "low",
      "mitigation": "Insert periodic human review checkpoints; use quality scoring to trigger review."
    }
  ]
}