{ "format": "agent-article-v0", "article_id": "EA-079", "canonical_url": "/tools/ea-079-digital-twin-ai", "page_type": "reference", "claim_ids": [], "evidence_ids": [ "EVD-EA-079-URL-7F534BA693CC", "EVD-EA-079-URL-FF03E6B6B61B", "EVD-EA-079-URL-B492CD7908BD", "EVD-EA-079-URL-1CFB4311FF37", "EVD-EA-079-URL-4E0754625EE0", "EVD-EA-079-URL-8767DD679FAE", "EVD-EA-079-URL-74D9927B7224", "EA-079-R11-RESEARCH", "EA-079-R11-WEB-READBACK" ], "unknown_ids": [ "EA-079-UNKNOWN-01", "EA-079-UNKNOWN-CURRENTNESS" ], "operational_object_ids": [], "provider": [], "models": [], "plans": [], "surfaces": [], "regions": [], "current_status": "UNKNOWN", "observation_status": "SPEC_ONLY", "freshness": "UNKNOWN", "last_verified": null, "supersedes": [], "superseded_by": [], "provenance": [ "COMM-074-EA0079", "SRC-EA-079-URL-7F534BA693CC", "SRC-EA-079-URL-FF03E6B6B61B", "SRC-EA-079-URL-B492CD7908BD", "SRC-EA-079-URL-1CFB4311FF37", "SRC-EA-079-URL-4E0754625EE0", "SRC-EA-079-URL-8767DD679FAE", "SRC-EA-079-URL-74D9927B7224", "WEBBODY-EA0079-R8-B9A70854" ], "article_sources": [ { "id": "EVD-EA-079-URL-7F534BA693CC", "evidence_class": "RESEARCH", "title": "author/org: ISO/TC 184/SC 4", "href": "https://www.iso.org/standard/87426.html", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-7F534BA693CC" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://www.iso.org/standard/87426.html" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-079-URL-FF03E6B6B61B", "evidence_class": "RESEARCH", "title": "author/org: ISO/TC 184/SC 4", "href": "https://www.iso.org/standard/90387.html", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-FF03E6B6B61B" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://www.iso.org/standard/90387.html" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-079-URL-B492CD7908BD", "evidence_class": "RESEARCH", "title": "author/org: Microsoft", "href": "https://learn.microsoft.com/en-us/azure/digital-twins/concepts-models", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-B492CD7908BD" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://learn.microsoft.com/en-us/azure/digital-twins/concepts-models" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-079-URL-1CFB4311FF37", "evidence_class": "RESEARCH", "title": "author/org: Amazon Web Services", "href": "https://docs.aws.amazon.com/iot-twinmaker/latest/guide/tm-knowledge-graph-scene.html", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-1CFB4311FF37" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://docs.aws.amazon.com/iot-twinmaker/latest/guide/tm-knowledge-graph-scene.html" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-079-URL-4E0754625EE0", "evidence_class": "RESEARCH", "title": "author/org: NVIDIA", "href": "https://nvidianews.nvidia.com/news/nvidia-agent-toolkit-expands-with-new-omniverse-libraries-putting-ai-agents-to-work-building-simulation-ready-worlds", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-4E0754625EE0" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://nvidianews.nvidia.com/news/nvidia-agent-toolkit-expands-with-new-omniverse-libraries-putting-ai-agents-to-work-building-simulation-ready-worlds" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-079-URL-8767DD679FAE", "evidence_class": "RESEARCH", "title": "author/org: NVIDIA", "href": "https://docs.omniverse.nvidia.com/dev-overview/latest/branches/production.html", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-8767DD679FAE" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://docs.omniverse.nvidia.com/dev-overview/latest/branches/production.html" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-079-URL-74D9927B7224", "evidence_class": "RESEARCH", "title": "author/org: NVIDIA", "href": "https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-079-URL-74D9927B7224" ], "supporting_locators": [ "EVIDENCE TABLE", "canonical URL: https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EA-079-R11-RESEARCH", "evidence_class": "RESEARCH", "title": "EA-079 監査済みresearch / canonical revision binding", "supports": [ "1. EA-079は成立する。ただしDigital Twinを「3Dコピー」ではなく、最低でも asset/model identity + state synchronization + relationship/topology + simulation/behavior model + scenario/action + outcome + real-world promotion gate の組として定義すべきである。\n2. Agent接続時の中心価値は、LLMに物理法則を推測させることではなく、候補actionをformal/physics/process simulatorへ渡し、simulation outcomeを検査してからhuman/policy gate経由で現実へ昇格することにある。\n3. ISO 23247-6:2026によりmulti-twin composition/interoperationは標準化対象であり、provider固有twinを共通Coreへ写像する根拠がある。\n4. Azure Digital Twinsはsemantic graph/state modeling、AWS TwinMakerはknowledge graph/scene integration、NVIDIA Omniverseはphysics/sensor simulation寄りであり、「Digital Twin製品」を同じ能力として横並びにしない。semantic twin / operational twin / physics twin / visualization twinを能力軸で分離する。\n5. Agent simulationでは、twinが返した結果だけでなく、入力state snapshot、scenario、model/solver/physics version、random seed（該当時）、constraints、acceptance criteriaを保存しなければ監査可能なpre-execution evidenceにならない。" ], "limit": "canonical監査・revision bindingの存在を示す。外部ソースcurrentnessが本文またはcontrol-planeで確定していない場合は、currentnessをUNKNOWNのまま保持する。", "origin_class": "AUDITED_CANONICAL_RESEARCH_PACKET", "source_or_experiment_ids": [ "COMM-074-EA0079" ], "supporting_locators": [ "COMM-074-EA0079", "EA-079-R5-LEGACY-MIGRATION-20261008-R1", "content/staging/EA-079.md" ], "scope": "監査済みEA本文・canonical revision・既存Source Graph relationの範囲。欠損currentnessは推測で閉じない。", "provenance_status": "AUDIT_PASS_CANONICAL_BINDING" }, { "id": "EA-079-R11-WEB-READBACK", "evidence_class": "IMPL", "title": "EA-079 Web body identity / canonical readback", "supports": [ "canonical staging bodyのSHA-256一致とR9 canonical ledger readbackをProduction projectionの入力として検証する。" ], "limit": "body identity/readbackは本文claim自体の真偽や外部ソースの最新性を追加保証しない。", "checked_at": null, "origin_class": "IMPLEMENTATION_READBACK", "source_or_experiment_ids": [ "WEBBODY-EA0079-R8-B9A70854" ], "supporting_locators": [ "WEBBODY-EA0079-R8-B9A70854", "content/staging/EA-079.md", "EA-079-R5-LEGACY-MIGRATION-20261008-R1" ], "scope": "R9/R10 canonical readback後のProduction生成時body identity。", "provenance_status": "CANONICAL_READBACK_BODY_HASH_MATCH" } ], "research_sections": [ { "heading": "WHY_THIS_MATTERS_TO_UPDATE", "text": "Agentが設備・工場・ネットワーク・建物・物流などのphysical/operational systemへ作用する場合、LLMのtool-call成功だけでは安全性・実行可能性を保証できない。Digital Twinは、現実のasset/stateをモデル化し、候補actionをsimulationで評価してから実環境へ昇格させる境界になり得る。ただしtwinは現実そのものではなく、model fidelity・state freshness・boundary conditions・sensor qualityに依存するため、「twinで成功した＝現実でも安全」を保証するものではない。" }, { "heading": "SCOPE_ROUTING", "text": "Enterprise AI Core: simulation-before-action、state/provenance、approval gate、rollback/abort、model-version governance。\nProvider mapping: Azure Digital Twins、AWS IoT TwinMaker、NVIDIA Omniverse/physical AI。\nInteroperability: ISO 23247 series、DTDL/JSON-LD、複数twin composition。\nImplementation / non-LLM component: graph/model store、telemetry ingestion、physics/process simulator、scenario runner、policy/solver、3D/sensor simulation。\nGovernance: twin fidelity、state freshness、simulation-to-real promotion、audit。" }, { "heading": "CLAIM_OR_RESEARCH_QUESTION", "text": "Q1. Digital TwinをAgentのpre-execution safety/evaluation boundaryとして設計できるか。\nQ2. twin state・model・scenario・solver/physics versionを何まで固定すれば再現可能なdecision evidenceになるか。\nQ3. simulation successをreal-world safetyへ外挿できないfailure envelopeは何か。\nQ4. 複数vendor/assetのtwin compositionをどうinteroperableに扱うか。" }, { "heading": "EVIDENCE TABLE", "text": "1) source class: international standard\nexact title: ISO 23247-6:2026 Automation systems and integration — Digital twin framework for manufacturing — Part 6: Digital twin composition\nauthor/org: ISO/TC 184/SC 4\ncanonical URL: https://www.iso.org/standard/87426.html\npublication/update/access: published 2026-07 / accessed 2026-09-16\nversion: ISO 23247-6:2026 Edition 1\nsupporting locator: Abstract / composition types\nlabel: VERIFIED_SPEC\nrelation: support / context\nestablished: manufacturing digital twinsのcompositionとしてintegrated / unified / federatedを識別し、異なるparty/vendorのtwin間communication・aggregation・interoperationを扱う。単一vendorの3D sceneだけをDigital Twin一般形とみなさない根拠になる。\n\n\n2) source class: international standard use case\nexact title: ISO/TR 23247-100:2025 — Use case on management of semiconductor ingot growth process\nauthor/org: ISO/TC 184/SC 4\ncanonical URL: https://www.iso.org/standard/90387.html\npublication/update/access: published 2025-05 / accessed 2026-09-16\nversion: Edition 1\nsupporting locator: Abstract\nlabel: VERIFIED_SPEC\nrelation: support / context\nestablished: ISO 23247 seriesを用いてsemiconductor ingot growthのmonitoring/control twinを分析・設計する具体use caseが存在する。Digital Twinがvisualizationだけでなくmonitor/control system設計へ接続し得ることを支持する。\n\n\n3) source class: official product documentation\nexact title: DTDL models - Azure Digital Twins\nauthor/org: Microsoft\ncanonical URL: https://learn.microsoft.com/en-us/azure/digital-twins/concepts-models\npublication/update/access: current page accessed 2026-09-16\nversion: DTDL v2/v3; v3 recommended\nsupporting locator: Supported DTDL versions; model properties/components/relationships\nlabel: VERIFIED_SPEC + PRODUCT_VOLATILE\nrelation: support / limit\nestablished: Azure Digital TwinsはJSON-LD based DTDLでproperties/components/relationshipsを持つmodelを定義し、v2/v3をsupportする。v3推奨。一方Azure Digital Twins Explorerのv3 supportにはModel Graph/import等の制限があり、service capabilityとUI capabilityを分離する必要がある。\n\n\n4) source class: official product documentation\nexact title: Knowledge graph scene integration / Dynamic scenes - AWS IoT TwinMaker\nauthor/org: Amazon Web Services\ncanonical URL: https://docs.aws.amazon.com/iot-twinmaker/latest/guide/tm-knowledge-graph-scene.html\npublication/update/access: accessed 2026-09-16\nversion: current service docs\nsupporting locator: knowledge graph relationships; scene viewer; dynamic scene behavior\nlabel: VERIFIED_SPEC + PRODUCT_VOLATILE\nrelation: support / limit\nestablished: TwinMakerはentity relationship knowledge graphと3D scene/viewerを接続できる。dynamic sceneはknowledge graph queryに基づきscene node/settings/data bindingを更新する。visual/graph integrationの存在は確認できるが、それ自体はphysics fidelityやAgent action safetyを保証しない。\n\n\n5) source class: official vendor release/product announcement\nexact title: NVIDIA Agent Toolkit Expands With New Omniverse Libraries, Putting AI Agents to Work Building Simulation-Ready Worlds\nauthor/org: NVIDIA\ncanonical URL: https://nvidianews.nvidia.com/news/nvidia-agent-toolkit-expands-with-new-omniverse-libraries-putting-ai-agents-to-work-building-simulation-ready-worlds\npublication/update/access: 2026-07-20 / accessed 2026-09-16\nversion/release: Omniverse libraries / Agent Toolkit, 2026-07 announcement\nsupporting locator: RTX sensor simulation; GPU-accelerated physics; simulation-ready asset validation; agent-callable tools\nlabel: SOURCE_REPORT + PRODUCT_VOLATILE\nrelation: support / context\nestablished: NVIDIAはOmniverse librariesをAgent Toolkitへ接続し、sensor simulation、physics simulation、simulation-ready asset validationをagent-callable workflowとして公開している。AI agentとsimulation environmentの技術接続がcurrent product directionとして存在する。\n\n\n6) source class: official product/release documentation\nexact title: Production Branch (PB) — Omniverse Developer Overview\nauthor/org: NVIDIA\ncanonical URL: https://docs.omniverse.nvidia.com/dev-overview/latest/branches/production.html\npublication/update/access: accessed 2026-09-16\nversion/release: Production Branch May 2026 (PB 26h1); planned EOS 2027-01; EOL 2027-04\nsupporting locator: PB 26h1; Agentic AI Development; Sensor Simulation\nlabel: VERIFIED_SPEC + PRODUCT_VOLATILE\nrelation: support / availability gate\nestablished: stable production branchにもagent-native platform servicesとsensor simulationが入っており、version/support windowを固定可能。Digital Twin evidenceではsceneだけでなくsimulation runtime/library branchを記録すべき。\n\n\n7) source class: official vendor blueprint/release\nexact title: NVIDIA Releases Vera Rubin DSX AI Factory Reference Design and Omniverse DSX Digital Twin Blueprint With Broad Industry Support\nauthor/org: NVIDIA\ncanonical URL: https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support\npublication/update/access: 2026-03-16 / accessed 2026-09-16\nversion/release: Omniverse DSX Blueprint GA, 2026-03\nsupporting locator: DSX Sim / SimReady / simulation before construction or deployment\nlabel: SOURCE_REPORT + PRODUCT_VOLATILE\nrelation: support / limit\nestablished: AI factoryのpower/cooling/network/operations等をsimulationし、hardware/workload changeをproduction disruptionなしに評価するcommercial blueprintがGAと報告される。ただしvendor-reported benefitは独立実運用performanceの証明ではない。" }, { "heading": "FINDINGS", "text": "1. EA-079は成立する。ただしDigital Twinを「3Dコピー」ではなく、最低でも asset/model identity + state synchronization + relationship/topology + simulation/behavior model + scenario/action + outcome + real-world promotion gate の組として定義すべきである。\n2. Agent接続時の中心価値は、LLMに物理法則を推測させることではなく、候補actionをformal/physics/process simulatorへ渡し、simulation outcomeを検査してからhuman/policy gate経由で現実へ昇格することにある。\n3. ISO 23247-6:2026によりmulti-twin composition/interoperationは標準化対象であり、provider固有twinを共通Coreへ写像する根拠がある。\n4. Azure Digital Twinsはsemantic graph/state modeling、AWS TwinMakerはknowledge graph/scene integration、NVIDIA Omniverseはphysics/sensor simulation寄りであり、「Digital Twin製品」を同じ能力として横並びにしない。semantic twin / operational twin / physics twin / visualization twinを能力軸で分離する。\n5. Agent simulationでは、twinが返した結果だけでなく、入力state snapshot、scenario、model/solver/physics version、random seed（該当時）、constraints、acceptance criteriaを保存しなければ監査可能なpre-execution evidenceにならない。" }, { "heading": "LIMITS / COUNTEREVIDENCE / FAILURE ENVELOPE", "text": "- MODEL GAP: twinに未実装の物理・運用制約はsimulationで検出できない。\n- STATE STALENESS: telemetry遅延・欠損・sensor driftによりtwin stateが現実と乖離する。\n- PARAMETER/CALIBRATION ERROR: geometry、mass、thermal、network、queue/service-time等のparameter誤りで結果が変わる。\n- SIM2REAL GAP: simulation successはreal-world safety/performanceの保証ではない。現実側のdisturbance、wear、human behavior、unknown unknownsを含まない場合がある。\n- COMPOSITION ERROR: 複数twinをfederateしてもidentity、units、timebase、CRS、ontology、causal semanticsが不一致なら誤った統合状態になる。\n- AGENT FORMALIZATION ERROR: 自然言語要求をscenario/action/constraintへ変換するLLMが誤れば、simulatorが正しくても誤った問題を検証する。\n- VENDOR CLAIM LIMIT: NVIDIA等のproduct announcementにある効率改善値・導入効果は当該条件のvendor reportであり一般化しない。\n- UI LIMIT: Azure Digital Twins ExplorerのDTDL v3制限のように、backend supportと管理UI supportは一致しない。" }, { "heading": "AVAILABILITY_GATES", "text": "Azure: Azure Digital Twins region/service availability、DTDL v2/v3、API/SDK/CLI vs Explorer機能差、tenant/auth/configuration。\nAWS: TwinMaker pricing tier、workspace/knowledge graph/scene configuration、Grafana/custom viewer、source connector availability。\nNVIDIA: Omniverse library/Production Branch、GPU/RTX requirements、AI Enterprise/Omniverse support entitlement、specific simulation library、local/cloud deployment。\nCross-vendor: asset model availability、telemetry access、simulation-ready geometry/materials、calibration data、license、OT network boundary。" }, { "heading": "IMPLEMENTATION_STATE", "text": "VERIFIED_SPEC: ISO 23247-6:2026 composition framework; Azure DTDL v2/v3 modeling; TwinMaker knowledge-graph/scene integration; Omniverse PB 26h1 support lifecycle.\nSOURCE_REPORT / PRODUCT_VOLATILE: NVIDIA Agent Toolkit/Omniverse agent-callable simulation and DSX blueprint GA claims.\nPROPOSAL: Agent actionをDigital Twinでpreflightする共通simulation gateと下記contract。\n\n\nCore candidate: DIGITAL TWIN ACTION CONTRACT\n- physical/operational asset ID + authoritative source\n- twin/model ID + schema/ontology version\n- twin composition type / dependency twins\n- state snapshot ID; event time; ingestion time; freshness SLA\n- telemetry/source provenance; unit; coordinate/time reference\n- calibration/parameter set version\n- simulator/physics/process/solver engine + version/build\n- scenario ID; candidate action/tool call; constraints\n- deterministic/random mode; seed if applicable\n- predicted outcome + uncertainty/range\n- safety/policy checks + acceptance criteria\n- simulation result: pass/fail/unknown/inconclusive\n- human/automated approval identity and policy version\n- real-world execution ID or rejection\n- observed post-action outcome\n- twin-vs-real residual/drift and recalibration record" }, { "heading": "BENCHMARK_OR_EVAL", "text": "単一benchmark scoreでは閉じない。後続TEST_PROFILE:\nA. golden synthetic scenarios: known feasible/infeasible actionを用意しsimulation gateのfalse-pass/false-blockを測る。\nB. historical replay: 過去telemetry/actionをreplayし、predicted outcomeとobserved outcomeのresidualを測る。\nC. stale-state injection: telemetry delay/dropout/driftを注入し、freshness gateがunsafe promotionを止めるか測る。\nD. model-gap fixtures: twinにないconstraintを意図的に設定し、UNKNOWN/inconclusiveへ退避できるか測る。\nE. composition fixtures: unit/timebase/identity mismatchを含む複数twin federationを試しvalidation failureを測る。\nF. agent formalization: human-authored canonical scenario vs LLM-generated scenarioを比較し、constraint omission/addition rateを測る。\nmetrics: false simulation pass, false block, state freshness violation, scenario formalization error, constraint coverage, sim-to-real residual, calibration drift, p50/p95 simulation latency, compute cost/scenario, audit completeness。\n実設備actionは未実施。read-only replay / synthetic simulationまでをsafe testとする。" }, { "heading": "SECURITY / PRIVACY / AUDIT / COST / OPERABILITY", "text": "Security: Digital TwinがOT/設備controlへ接続する場合、simulation environmentとproduction actuator credentialを分離し、simulation passだけで直接actuationしない。least privilege + explicit promotion gateを要求。\nPrivacy: building/worker/location/telemetryを含む場合はpersonal/operational sensitive dataのscopeを別管理。\nAudit: state snapshot、model/version、scenario、simulation output、approval、real execution、post-action observationを一連のtraceとして保持。\nCost: 3D/physics/sensor simulationはLLM token costとは別にGPU、simulation time、telemetry storage、scene/graph、engineering/calibration costを持つ。\nOperability: twin fidelityを継続監視し、sim-to-real residualがthreshold超過した場合は自動promotionを停止してrecalibrationへ戻す。" }, { "heading": "SAFE_WORDING_FOR_FUTURE_BOOK", "text": "「Digital Twinは、AI Agentが現実の設備や運用へ作用する前に候補判断を仮想環境で検証するための一つの境界として利用できる。ただし、Digital Twinは現実そのものではない。安全性は、モデルに含まれる制約、状態の鮮度、校正、simulation engine、scenario変換の正しさに依存する。したがって『simulationで成功したから安全』ではなく、『どの状態・モデル・条件で何を試し、何が未検証か』を証跡化し、現実への実行には別の承認・policy gateを置く。」" }, { "heading": "UPDATE_WORK_HANDOFF", "text": "1. 公開後更新ではEA-076 Time-series/Sensor AIと接続し、telemetry state freshness/calibration contractを共有する。\n2. EA-078 Solver/Rule Engineと接続し、Digital Twin simulation結果のaccept/reject policyをformal engineへ委ねる構成を比較する。\n3. EA-069 Knowledge Graphと接続するが、relationship graphとphysics/behavior modelを同一視しない。\n4. Azure Digital Twins / AWS IoT TwinMaker / NVIDIA Omniverseを semantic graph / visualization / process/physics/sensor simulation / agent integration の能力matrixで再採集する。\n5. ISO 23247 seriesのPart 1〜6と2025/2026 use-case TRを再確認し、manufacturing以外へ一般化する際は適用範囲を明記する。\n6. 後続live testは実設備を使わず、synthetic twinまたは公開sample sceneでscenario→simulation→policy gate→no-op promotionまでを再現する。" }, { "heading": "OPEN_UNKNOWN", "text": "- AWS IoT TwinMakerの2026-09-16時点のexact pricing tier/service quotas/API revisionは今回固定していない。\n- Azure Digital Twinsのregion-by-region current availability、pricing、API versionはprovider mapping時に再採集する。\n- NVIDIA ovphysx/ovrtx等のexact package release/tag/commitは後続implementation採集で固定する。\n- independent production evidenceとして、Agent判断をDigital Twinでpreflightし事故率/false actionを比較した公開運用研究は今回十分な一次資料を確保していない。\n- Digital Twinでの成功がreal-world action safetyをどの程度予測するかはdomain-specificであり一般値UNKNOWN。" }, { "heading": "STOP_CONDITION", "text": "ISO standard + 3 provider implementation classes + simulation/agent integration + failure envelope + safe TEST_PROFILEが揃ったためEA-079の初期Evidence Packetはbounded complete。上記UNKNOWNは公開後provider/domain mapping時に再採集する。\n\n---" }, { "heading": "AUDITED_CURRENTNESS_BOUNDARY", "text": "ISO 23247-6:2026 manufacturing digital-twin composition scope remains bounded and does not prove generic agent safety or twin accuracy." } ], "human_url": "/tools/ea-079-digital-twin-ai", "markdown_url": "/agent/article.md?path=%2Ftools%2Fea-079-digital-twin-ai", "json_url": "/agent/article.json?path=%2Ftools%2Fea-079-digital-twin-ai" }