{
  "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": "2610.11899",
  "title": "Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System",
  "summary": "It replaces ad-hoc, label-driven selection among chatbot, RAG, copilot, workflow, and agent designs with a common vocabulary that classifies seven recurring forms by architectural pattern, control of execution, number of agent calls, and tool use. It costs nothing in memory or latency to apply, but adopting its implied shift from user-confirmed copilot loops to AI-planned autonomous execution increases orchestration complexity, API spend per task, and failure surface. What can fail in practice is over-adopting multi-step agentic forms where a simpler single-call or RAG form would suffice, yielding higher cost and harder debugging without measurable benefit.",
  "rationale": "The survey provides no new technique or benchmark but offers a decision vocabulary that a constrained team can apply immediately to choose the cheapest sufficient architecture for production features.",
  "technique": "Taxonomy of LLM-integrated application architectures",
  "family": "orquestracao_multiagente",
  "recommendation": "adotar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11899",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
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      "claim_id": "claim-01",
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      "dimension": "security",
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    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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  "equations_fetched_at": "",
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}