{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2610.06044",
  "title": "RocketAgent: A Long-Horizon Engineering Agent for Multidisciplinary Design of Liquid-Rocket Thrust Chambers",
  "summary": "RocketAgent replaces ad hoc, engineer-driven coordination of heterogeneous simulation tools with a single planning agent that owns a typed design representation, selects methods via a provenance-aware knowledge graph, and blocks superseded inputs when design decisions are revised. The cost is substantial integration engineering: wrapping each domain tool as a callable skill, maintaining the design IR and approval gates, and accepting LLM latency and API spend across a long-horizon workflow. Failure modes include incorrect dependency invalidation propagating stale results, wrong method selection from the knowledge graph, and agent-initiated operating-point changes that require engineer judgment, as the paper itself gates such revisions on human approval.",
  "rationale": "The paper demonstrates one representative simulation case plus controlled mechanism tests with no released artifacts, no quantified gain, and heavy domain-specific tool integration, so a small team with a 24 GB GPU should track the architectural patterns (typed shared state, revision-aware invalidation) rather than adopt the system.",
  "technique": "Plan-owning agent with typed design IR and revision invalidation",
  "family": "planejamento_e_decomposicao",
  "recommendation": "observar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06044",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RocketAgent replaces ad hoc, engineer-driven coordination of heterogeneous simulation tools with a single planning agent that owns a typed design representation, selects methods via a provenance-aware knowledge graph, and blocks superseded inputs when design decisions are revised. The cost is substantial integration engineering: wrapping each domain tool as a callable skill, maintaining the design IR and approval gates, and accepting LLM latency and API spend across a long-horizon workflow. Failure modes include incorrect dependency invalidation propagating stale results, wrong method selection from the knowledge graph, and agent-initiated operating-point changes that require engineer judgment, as the paper itself gates such revisions on human approval.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06044",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
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    {
      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}