{ "format": "agent-article-v0", "article_id": "EA-121", "canonical_url": "/tools/ea-121-llm", "page_type": "reference", "claim_ids": [], "evidence_ids": [ "EVD-EA-121-URL-EC7EDE4A4FCF", "EVD-EA-121-URL-7647307FE498", "EA-121-R11-RESEARCH", "EA-121-R11-WEB-READBACK" ], "unknown_ids": [ "EA-121-UNKNOWN-01", "EA-121-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-042-EA0121", "SRC-EA-121-URL-EC7EDE4A4FCF", "SRC-EA-121-URL-7647307FE498", "WEBBODY-EA0121-R8-547F9545" ], "article_sources": [ { "id": "EVD-EA-121-URL-EC7EDE4A4FCF", "evidence_class": "RESEARCH", "title": "source class: primary research paper; exact title: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation; author/org: Qingyun Wu et al.; canonical URL:", "href": "https://arxiv.org/abs/2308.08155", "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-121-URL-EC7EDE4A4FCF" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: primary research paper; exact title: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation; author/org: Qingyun Wu et al.; canonical URL: https://arxiv.org/abs/2308.08155" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-121-URL-7647307FE498", "evidence_class": "RESEARCH", "title": "source class: primary research paper; exact title: ReAct: Synergizing Reasoning and Acting in Language Models; author/org: Shunyu Yao et al.; canonical URL:", "href": "https://arxiv.org/abs/2210.03629", "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-121-URL-7647307FE498" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: primary research paper; exact title: ReAct: Synergizing Reasoning and Acting in Language Models; author/org: Shunyu Yao et al.; canonical URL: https://arxiv.org/abs/2210.03629" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EA-121-R11-RESEARCH", "evidence_class": "RESEARCH", "title": "EA-121 監査済みresearch / canonical revision binding", "supports": [ "AutoGen原著は、複数のcustomizable/conversable agentが会話し、LLM・human input・toolを組み合わせ、自然言語/コードでinteraction behaviorを定義する汎用基盤を確立する。これは異種model IDを役割別に割り当てる実装の根拠にはなるが、原著abstractは「異種にすれば必ず改善」「enterprise deploymentで障害分離できる」とは主張しない。ReActはreasoningとactingのinterleaveを示すため、異種分離が唯一の正解ではなく、役割分割と相互作用設計が選択問題である。" ], "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-042-EA0121" ], "supporting_locators": [ "COMM-042-EA0121", "EA-121-R5-LEGACY-MIGRATION-20261008-R1", "content/staging/EA-121.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-121-R11-WEB-READBACK", "evidence_class": "IMPL", "title": "EA-121 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-EA0121-R8-547F9545" ], "supporting_locators": [ "WEBBODY-EA0121-R8-547F9545", "content/staging/EA-121.md", "EA-121-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": "同一モデルの反復ではなく、異なるモデル/agent/toolに役割を割り当てると、相補性・障害分離・費用制御が期待できる一方、通信・評価・責任境界が増える。" }, { "heading": "SCOPE_ROUTING", "text": "multi-LLM / interoperability / implementation / evaluation / cost / security" }, { "heading": "CLAIM_OR_RESEARCH_QUESTION", "text": "AutoGen原著は複数agent、LLM/human/toolの組み合わせ、相互作用のプログラムを確立するか。異種modelの採用が常に相補性・品質向上を生むか。" }, { "heading": "EVIDENCE TABLE", "text": "- source class: primary research paper; exact title: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation; author/org: Qingyun Wu et al.; canonical URL: https://arxiv.org/abs/2308.08155; publication/update/access: submitted 2023-08-16, revised v2 2023-10-03 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: arXiv v2; supporting locator: abstract lines 8-20（open-source framework、multiple conversable agents、LLMs/human inputs/toolsの組合せ、interaction behaviorの定義、数学/coding/QA/operations research等の例）; evidence label: RESEARCH; relation: support / limit.\n- source class: primary research paper; exact title: ReAct: Synergizing Reasoning and Acting in Language Models; author/org: Shunyu Yao et al.; canonical URL: https://arxiv.org/abs/2210.03629; publication/update/access: submitted 2022-10-06, revised v3 2023-03-10 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: arXiv v3 / ICLR camera-ready; supporting locator: abstract lines 16-21（reasoning/acting interleaving、external source interaction、task trajectories、specific benchmark results）; evidence label: RESEARCH; relation: context / limit." }, { "heading": "FINDINGS", "text": "AutoGen原著は、複数のcustomizable/conversable agentが会話し、LLM・human input・toolを組み合わせ、自然言語/コードでinteraction behaviorを定義する汎用基盤を確立する。これは異種model IDを役割別に割り当てる実装の根拠にはなるが、原著abstractは「異種にすれば必ず改善」「enterprise deploymentで障害分離できる」とは主張しない。ReActはreasoningとactingのinterleaveを示すため、異種分離が唯一の正解ではなく、役割分割と相互作用設計が選択問題である。" }, { "heading": "LIMITS / COUNTEREVIDENCE / FAILURE ENVELOPE", "text": "agent数増加はtoken/tool/latency/costを増やし、誤り・prompt injection・悪意あるtool出力を通信で伝播し得る。研究例のtask/モデル/評価条件は企業の権限・秘密・SLAを表さない。異種性の定義（provider、checkpoint、prompt、tool、role）が曖昧だと原因分析不能。" }, { "heading": "AVAILABILITY_GATES", "text": "各model/providerのAPI・license・region・quota・data policy、orchestrator、tool permission、network、human approval、trace storeを確認する。認証済みproviderの組合せ実験は未実施。" }, { "heading": "IMPLEMENTATION_STATE", "text": "role contract、typed message、model ID/version、tool capability、budget、timeout、termination、escalation、provenanceを固定する設計候補。実装・障害注入試験未実施。" }, { "heading": "BENCHMARK_OR_EVAL", "text": "同一taskをsingle、homogeneous multi-agent、heterogeneous multi-agentで比較し、quality、cost、latency、tool error、cross-agent contamination、abstain、recoveryを別scoreにする。benchmark外挿なし。" }, { "heading": "SECURITY / PRIVACY / AUDIT / COST / OPERABILITY", "text": "SECURITY/PRIVACY: agent間message最小化・secret redaction・tool allowlist。AUDIT: model/version、role、message hash、tool call、approval、termination。COST: per-agent token/tool、人間レビュー。OPERABILITY: circuit breaker、max turns、dead-letter、replay。" }, { "heading": "SAFE_WORDING_FOR_FUTURE_BOOK", "text": "「AutoGen原著は複数agent、LLM/human/toolの組合せ、相互作用のプログラムを示す。異種LLM構成はその実装候補だが、異種化だけで品質・安全・費用対効果が保証されるわけではなく、task別比較が必要である。」" }, { "heading": "UPDATE_WORK_HANDOFF", "text": "本文ではagent topologyとmodel diversityを分離し、各edgeのinput/output contract・trust boundary・cost・termination・human gateを追加する。現行orchestrator/SDKは別PRODUCT_VOLATILE採集する。" }, { "heading": "OPEN_UNKNOWN", "text": "異種モデルの相補性の因果効果、最適agent数、failure propagation、cross-provider data handling、tool permission isolation、enterprise SLA。" }, { "heading": "STOP_CONDITION", "text": "複数agentの会話成功を正しさ・独立検証・安全な権限分離の証拠としない。\nUNIT_CLOSE_STATUS: INDIVIDUAL_BOUNDED_CLOSE / EVIDENCE_RECORDED / UNKNOWN_PRESERVED\n\n---" } ], "human_url": "/tools/ea-121-llm", "markdown_url": "/agent/article.md?path=%2Ftools%2Fea-121-llm", "json_url": "/agent/article.json?path=%2Ftools%2Fea-121-llm" }