{
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    "EVD-EA-085-URL-B76BA177074D",
    "EVD-EA-085-URL-EF8F677C69EC",
    "EVD-EA-085-URL-46FF421FAD2B",
    "EA-085-R11-RESEARCH",
    "EA-085-R11-WEB-READBACK"
  ],
  "unknown_ids": [
    "EA-085-UNKNOWN-01",
    "EA-085-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": [],
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    "COMM-078-EA0085",
    "SRC-EA-085-URL-6288847FCE17",
    "SRC-EA-085-URL-B76BA177074D",
    "SRC-EA-085-URL-EF8F677C69EC",
    "SRC-EA-085-URL-46FF421FAD2B",
    "WEBBODY-EA0085-R8-967A03E1"
  ],
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      "id": "EVD-EA-085-URL-6288847FCE17",
      "evidence_class": "RESEARCH",
      "title": "1. source class: standard/author-maintained specification; exact title: CloudEvents Specification; author/org: CNCF CloudEvents; canonical URL:",
      "href": "https://github.com/cloudevents/spec",
      "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-085-URL-6288847FCE17"
      ],
      "supporting_locators": [
        "EVIDENCE TABLE",
        "1. source class: standard/author-maintained specification; exact title: CloudEvents Specification; author/org: CNCF CloudEvents; canonical URL: https://github.com/cloudevents/spec"
      ],
      "scope": "EVIDENCE TABLE",
      "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED"
    },
    {
      "id": "EVD-EA-085-URL-B76BA177074D",
      "evidence_class": "RESEARCH",
      "title": "2. source class: official product documentation; exact title: Webhooks; author/org: OpenAI; canonical URL:",
      "href": "https://developers.openai.com/api/docs/guides/webhooks",
      "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-085-URL-B76BA177074D"
      ],
      "supporting_locators": [
        "EVIDENCE TABLE",
        "2. source class: official product documentation; exact title: Webhooks; author/org: OpenAI; canonical URL: https://developers.openai.com/api/docs/guides/webhooks"
      ],
      "scope": "EVIDENCE TABLE",
      "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED"
    },
    {
      "id": "EVD-EA-085-URL-EF8F677C69EC",
      "evidence_class": "RESEARCH",
      "title": "3. source class: author-maintained OSS documentation; exact title: Fault Tolerance; author/org: Apache Flink; canonical URL:",
      "href": "https://nightlies.apache.org/flink/flink-docs-stable/docs/learn-flink/fault_tolerance/",
      "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-085-URL-EF8F677C69EC"
      ],
      "supporting_locators": [
        "EVIDENCE TABLE",
        "3. source class: author-maintained OSS documentation; exact title: Fault Tolerance; author/org: Apache Flink; canonical URL: https://nightlies.apache.org/flink/flink-docs-stable/docs/learn-flink/fault_tolerance/"
      ],
      "scope": "EVIDENCE TABLE",
      "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED"
    },
    {
      "id": "EVD-EA-085-URL-46FF421FAD2B",
      "evidence_class": "RESEARCH",
      "title": "4. source class: author-maintained OSS documentation; exact title: Checkpointing; author/org: Apache Flink; canonical URL:",
      "href": "https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/checkpointing/",
      "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-085-URL-46FF421FAD2B"
      ],
      "supporting_locators": [
        "EVIDENCE TABLE",
        "4. source class: author-maintained OSS documentation; exact title: Checkpointing; author/org: Apache Flink; canonical URL: https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/checkpointing/"
      ],
      "scope": "EVIDENCE TABLE",
      "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED"
    },
    {
      "id": "EA-085-R11-RESEARCH",
      "evidence_class": "RESEARCH",
      "title": "EA-085 監査済みresearch / canonical revision binding",
      "supports": [
        "Event-driven AI should use an EVENT_TO_ACTION_CONTRACT rather than direct “event -> LLM -> side effect.” Minimum stages: authenticate/verify event -> normalize envelope -> authorize tenant/resource -> deduplicate/idempotency check -> persist event/inbox -> classify/filter -> invoke AI or deterministic logic -> validate result -> policy/approval gate -> idempotent side effect/outbox -> record outcome. Delivery “exactly once” cannot be assumed merely because the stream processor has exactly-once state semantics; external AI/API/business side effects need transactional or idempotent integration. Event time and processing time must be distinct, and late/replayed events require explicit policy."
      ],
      "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",
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        "COMM-078-EA0085"
      ],
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        "COMM-078-EA0085",
        "EA-085-R5-LEGACY-MIGRATION-20261008-R1",
        "content/staging/EA-085.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-085-R11-WEB-READBACK",
      "evidence_class": "IMPL",
      "title": "EA-085 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-EA0085-R8-967A03E1"
      ],
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        "WEBBODY-EA0085-R8-967A03E1",
        "content/staging/EA-085.md",
        "EA-085-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": "Email arrival, file updates, logs, sensor events, completed batch jobs, and business events can trigger AI without polling or human initiation. The main enterprise risk is not whether a model can be called from an event handler, but delivery semantics: duplicate, delayed, reordered, replayed, forged, or bursty events can multiply inference and side effects."
    },
    {
      "heading": "SCOPE_ROUTING",
      "text": "Enterprise AI Core / interoperability / architecture / implementation / security / audit / cost / operability"
    },
    {
      "heading": "CLAIM_OR_RESEARCH_QUESTION",
      "text": "What event envelope, delivery semantics, deduplication, replay, backpressure, signature verification, and action-idempotency contract is required before an AI workflow can safely react to events?"
    },
    {
      "heading": "EVIDENCE TABLE",
      "text": "1. source class: standard/author-maintained specification; exact title: CloudEvents Specification; author/org: CNCF CloudEvents; canonical URL: https://github.com/cloudevents/spec ; access date: 2026-09-16; release: Core Specification v1.0.2, stable tag ce@stable; locator: README / CloudEvents Documents; evidence label: VERIFIED_SPEC; relation: interoperability/support. CloudEvents defines a common event metadata format and bindings including HTTP, Kafka, MQTT, AMQP and others.\n2. source class: official product documentation; exact title: Webhooks; author/org: OpenAI; canonical URL: https://developers.openai.com/api/docs/guides/webhooks ; access date: 2026-09-16; revision: current docs; locator: delivery guidance / signature verification; evidence label: VERIFIED_SPEC / PRODUCT_VOLATILE; relation: implementation/limit/security. OpenAI documents real-time webhook events, Standard Webhooks delivery, signature verification, retries up to 72 hours with exponential backoff on unsuccessful delivery, rare duplicate deliveries, and webhook-id as deduplication key.\n3. source class: author-maintained OSS documentation; exact title: Fault Tolerance; author/org: Apache Flink; canonical URL: https://nightlies.apache.org/flink/flink-docs-stable/docs/learn-flink/fault_tolerance/ ; access date: 2026-09-16; version: stable docs current at access; locator: exactly once end-to-end; evidence label: OSS_PRACTICE / VERIFIED_SPEC; relation: support/limit. Flink distinguishes at-most-once, at-least-once, and exactly-once state effects; end-to-end exactly-once requires replayable sources and transactional or idempotent sinks.\n4. source class: author-maintained OSS documentation; exact title: Checkpointing; author/org: Apache Flink; canonical URL: https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/checkpointing/ ; access date: 2026-09-16; version: master/current docs; locator: checkpointing modes / backpressure; evidence label: OSS_PRACTICE; relation: implementation/context. Documents checkpoint storage, exactly-once/at-least-once modes, checkpoint timeout, externalized checkpoints, and unaligned checkpoints for backpressure."
    },
    {
      "heading": "FINDINGS",
      "text": "Event-driven AI should use an EVENT_TO_ACTION_CONTRACT rather than direct “event -> LLM -> side effect.” Minimum stages: authenticate/verify event -> normalize envelope -> authorize tenant/resource -> deduplicate/idempotency check -> persist event/inbox -> classify/filter -> invoke AI or deterministic logic -> validate result -> policy/approval gate -> idempotent side effect/outbox -> record outcome. Delivery “exactly once” cannot be assumed merely because the stream processor has exactly-once state semantics; external AI/API/business side effects need transactional or idempotent integration. Event time and processing time must be distinct, and late/replayed events require explicit policy."
    },
    {
      "heading": "LIMITS / COUNTEREVIDENCE / FAILURE ENVELOPE",
      "text": "Duplicate webhook causes duplicate model calls/actions; retry storm; forged event without signature verification; replay attack; event reordering; stale event after resource deletion; fan-out amplification; poison event; dead-letter accumulation; schema evolution; event loop where AI-generated update retriggers itself; burst traffic/rate-limit collapse; backpressure; partial side effect followed by retry; non-idempotent downstream API; model nondeterminism on replay; hidden cost multiplication."
    },
    {
      "heading": "AVAILABILITY_GATES",
      "text": "Producer webhook/event support; event schema/version; delivery guarantee and retry window; signature/auth mechanism; event bus/stream retention; replay support; ordering key/partition semantics; consumer concurrency; model rate limits; side-effect API idempotency; tenant policy; dead-letter/recovery path."
    },
    {
      "heading": "IMPLEMENTATION_STATE",
      "text": "CloudEvents provides an interoperability envelope; webhooks/event buses/stream processors provide delivery infrastructure; AI inference remains a consumer step. Current OpenAI webhooks demonstrate retries/duplicates/signatures in a provider-specific path. Flink provides a mature reference for stream-state fault tolerance but does not by itself make arbitrary external model/tool actions exactly-once."
    },
    {
      "heading": "TEST_PROFILE",
      "text": "inject valid, invalid-signature, duplicate, reordered, delayed, replayed, burst, poison, schema-v2, and loop-inducing events. Force consumer crash before/after model call and before/after side effect. Measure event-to-decision latency, duplicate inference rate, duplicate side-effect rate, lost-event rate, replay correctness, DLQ recovery, backlog age, rate-limit errors, cost amplification, and audit completeness."
    },
    {
      "heading": "SECURITY / PRIVACY / AUDIT / COST / OPERABILITY",
      "text": "Persist event_id, source, type, schema/spec version, subject/resource, tenant, event_time, receive/process time, signature verification result, authz decision, dedupe key/state, retry count, trace/correlation ID, model invocation ID/version/cost, decision artifact, approval, action idempotency key, side-effect result, DLQ/replay lineage. Apply least privilege to event consumers and downstream tools; treat event payload as untrusted input and possible prompt-injection carrier."
    },
    {
      "heading": "SAFE_WORDING_FOR_FUTURE_BOOK",
      "text": "“Event-driven AI replaces polling with event-triggered execution, but safe operation depends on delivery semantics more than model capability. Assume events can be duplicated, delayed, reordered, replayed, or forged; verify, persist, deduplicate, authorize, and make downstream effects idempotent.”"
    },
    {
      "heading": "UPDATE_WORK_HANDOFF",
      "text": "Add EVENT_TO_ACTION_CONTRACT and connect it to batch-completion events, file/email/log/sensor triggers, and agent actions. Use CloudEvents-compatible metadata where practical. Require idempotency at the business side-effect boundary, not only stream-state checkpointing. Re-pin provider webhook event types/retry windows/signature specs at publication."
    },
    {
      "heading": "OPEN_UNKNOWN",
      "text": "provider-by-provider event/webhook matrices, exact current Kafka/Flink release/tag pins, and tenant-specific event retention/residency. STOP_CONDITION: common Core event/action safety contract established; provider mappings can be refreshed independently.\n\n---"
    }
  ],
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