{
  "version": 1,
  "date": "2026-09-14T11:02:45.531776+00:00",
  "kind": "essay",
  "writer": {
    "provider": "openai",
    "model": "gpt-6-astra",
    "family": "gpt"
  },
  "reviewer": {
    "provider": "anthropic",
    "model": "global.anthropic.claude-fable-5-1",
    "family": "fable"
  },
  "effort": "max",
  "status": "approved",
  "input_sha256": "4d35ceb81ae686d174558ea4dc6f107a9ae4878cb27c07ff5f9e2a4de3d7fc64",
  "evidence_sha256": "7b45f99c8b4b59cbdd060486b32a510b3f76fd09a9b0263bb797b985373f2c7d",
  "figure_sha256": [],
  "summary": "Checked the essay against the fetched abstracts of arXiv:2609.13136 (From Review to Reuse / Trace2Flow), arXiv:2609.12482 (CIVIC-AI augmentation framework), and NBER w31161 (Generative AI at Work). The topic is a human-AI system (human review and correction of AI agents' completed work); three independent open sources are used, and the Trace2Flow HCI paper and the NBER field study both qualify as technical sources; no social posts are cited. The main defect was that the draft told readers the supplied excerpts were 'bibliographic listings, not findings' even though full abstracts with methods and results were fetched, and it engaged only with the papers' titles. That editorial meta-commentary was removed and replaced with accurate, qualified summaries of what each paper reports, with explicit boundaries on what none of them tested. A standalone bolded slogan line was folded into plain prose, NBER figures were added from the abstract, the design proposal was tied to the CIVIC-AI conditions and the Trace2Flow cross-checking result as inference rather than as findings, and the BLUF context was aligned with the body's scope. All citations remain clickable and no sources were added beyond the evidence.",
  "changes": [
    "Removed the false and unpublishable statement that the supplied excerpts were bibliographic listings without findings; both arXiv abstracts were fetched and contain reported methods and results.",
    "Added an accurate account of From Review to Reuse: analysis of 10,803 n8n workflow templates, the Trace2Flow research probe, the N=20 study, improved understanding and error detection versus a prompt-only condition, validation succeeding mainly through cross-checking multiple evidence sources, and follow-up adaptation of workflows matching prompt adaptation in success, time, and difficulty while often preferred.",
    "Added an accurate account of When Does AI Augment Work?: its status as a CIVIC-AI 2026 workshop whitepaper, its critique of automation-capability and adoption metrics, its six augmentation conditions, and its social-survey case study.",
    "Stated that neither arXiv paper claims to test whether reviewer corrections accumulate into shared knowledge over many runs, so the essay's proposal is framed as a design thesis extending their framing.",
    "Expanded the Generative AI at Work paragraph with figures from the abstract (5,179 agents; 14% average productivity gain; 34% for novice and low-skilled workers; minimal for experienced workers) and the authors' suggestive evidence about disseminating best practices, while clarifying the study is not a test of post-task reuse.",
    "Replaced the standalone bolded thesis line with plain prose integrated into the argument.",
    "Connected the inspect/amend/disable/roll-back requirement to the CIVIC-AI conditions of meaningful human control and accountability and recovery, and connected the policy-checking recommendation to the Trace2Flow cross-checking result, both framed as inference.",
    "Tied the proposed evaluation metrics to the CIVIC-AI condition of durable net value without asserting anything about the studies' unobserved designs.",
    "Rewrote the closing sentences as plain prose and adjusted the BLUF context so it matches the essay's scope of agent review generally, with customer support as the example."
  ],
  "issues": [],
  "reviewed_sha256": "8b3b3c026e4dcece4ddbcca20da33e95a41e6bc522203874c3a4e37a8de06046",
  "code_changed": false
}