THIS IS JUST A REFERENCE TEMPLATE WITH EXAMPLES

Every engine specification shall consistently include:

Runtime Pipeline
Processing Algorithm
Resolution Strategies
Merge Strategy
Failure Matrix
Performance Characteristics

Every engine specification should also end with the same four implementation chapters:

API Specification
Observability & Metrics
Security & Replay
Testing & Compliance

This would make all ~300–500 future engine specifications follow a consistent structure, so developers always know where to find runtime contracts, operational metrics, replay behavior, and validation requirements.

It is the canonical specification pattern for all ZAYAZ micro-engines. Each engine should follow the same lifecycle:

Frontmatter & Architecture
Runtime Processing & Algorithms
Data Contracts, APIs & Metrics
Replay, Federation, AI, Security & Operations

Example:

1. Purpose

The TrustGate Context Enrichment Engine transforms a normalized TrustGate signal into a fully contextualized assurance object.

Unlike the preceding Parser and Normalizer engines—which focus on syntactic correctness and canonical representation—this engine establishes the complete operational context required for downstream trust evaluation.

The engine enriches incoming signals with:

organizational identity
historical assurance context
governance policies
replay history
federation metadata
lineage information
AI-derived recommendations
applicable schemas
software provenance
routing hints

The resulting object becomes the canonical TrustGate runtime entity processed by every remaining TrustGate micro-engine.

2. Position within TrustGate Runtime

Signal Parser
      │
      ▼
Signal Normalizer
      │
      ▼
──────────────────────────────────────────
 Context Enrichment Engine
──────────────────────────────────────────
      │
      ▼
Schema Validation
      │
      ▼
Business Validation
      │
      ▼
Trust Scoring
      │
      ▼
Decision Engine

This engine is therefore the bridge between:

data acquisition
organizational intelligence
governance
assurance
AI
federation

3. Responsibilities

The engine is responsible for enriching every incoming signal using trusted information retrieved from the ZAYAZ federation.

Primary responsibilities include:

Responsibility	Description
Identity Resolution	Resolve ECO Number, DID and organization metadata.
Trust Context	Load previous TrustGate scores and trust evolution.
Governance Context	Attach applicable FAGF policy versions and rule bundles.
Replay Context	Load replay history and replay confidence.
Lineage Context	Resolve DAL and USO lineage references.
Federation Context	Attach federation metadata and regional trust information.
AI Context	Retrieve DSAIL recommendations and calibration metadata.
Component Context	Resolve CMI, CSI and MEID references from ZAR.
Schema Context	Retrieve canonical schemas from SSSR.
Routing Context	Produce hints for downstream ZSSR routing.

4. Runtime Architecture

                 ┌─────────────────┐
                 │ Normalized Input│
                 └────────┬────────┘
                          │
                          ▼
               Context Enrichment Engine
                          │
      ┌────────────────────────────────────────┐
      │                                        │
      ▼                                        ▼

 Identity Resolver                    Lineage Resolver

      │                                        │
      ▼                                        ▼

 Governance Loader                  Federation Resolver

      │                                        │
      ▼                                        ▼

 AI Context Resolver                 Replay Resolver

      │                                        │
      └──────────────┬─────────────────────────┘
                     ▼

             Context Merge Engine

                     ▼

             Enriched Trust Signal

5. Runtime Lifecycle

Each signal passes through the following lifecycle:

Phase	Description
Receive	Accept normalized TrustGate object.
Resolve Identity	Resolve ECO, organization and DID metadata.
Resolve Lineage	Load DAL anchors and USO lineage graph.
Resolve Governance	Load applicable governance bundle.
Resolve Federation	Retrieve federation metadata from EGFS.
Resolve AI	Load DSAIL recommendations and calibration state.
Resolve Replay	Load replay confidence and assurance history.
Merge Context	Produce a unified enrichment object.
Emit	Publish enriched signal for validation.

6. External Dependencies

The Context Enrichment Engine is intentionally lightweight with respect to computation but highly connected.

System	Purpose
ZAR	Resolve CMIs, CSIs, MEIDs, software artifacts and provenance.
USO	Retrieve operational lineage and event history.
DAL	Retrieve ledger anchors and verification proofs.
SSSR	Load canonical schemas and semantic mappings.
DSAIL	AI recommendations and adaptive calibration.
FAGF	Governance rules and active policy bundles.
EGFS	Federation metadata and node trust information.
AII	Historical assurance intelligence metrics.
OARM	Runtime overrides and policy exceptions.
ZSSR	Routing hints for downstream orchestration.

7. Engine Registration

Property	Value
MEID	MEID_SIGNAL_ENRICH_CONTEXT
CMI	vera.TG-ENRICH.ENGINE.CONTEXT.1_0_0
Engine Type	Context Enrichment
Runtime Category	TrustGate
Registry	ZAR
Runtime Tier	Tier-0 Assurance Runtime

8. Design Principles

The engine follows several architectural principles:

deterministic enrichment
replay reproducibility
immutable enrichment records
federation-aware context
policy-driven enrichment
AI-assisted but human-verifiable recommendations
cryptographically verifiable provenance
minimal latency
horizontally scalable execution
zero trust between federation nodes

Every enrichment decision must itself be explainable and reproducible during future replay operations.

9. Downstream Consumers

The enriched runtime object is consumed by:

Schema Validation Engine
Business Rule Validation Engine
Trust Scoring Engine
Decision Engine
Replay Engine
DAL Anchor Engine
AII Engine
Assurance Analytics Engine
Federation Synchronization Engine

No TrustGate trust score shall be computed before successful completion of this enrichment stage.

10. Runtime Processing Pipeline

The Context Enrichment Engine executes a deterministic enrichment pipeline consisting of multiple independent enrichment stages.

Each stage contributes additional metadata while preserving immutability of previously attached context.

Receive Normalized Signal
          │
          ▼
Identity Resolution
          │
          ▼
Organization Context
          │
          ▼
Schema Context
          │
          ▼
Lineage Context
          │
          ▼
Governance Context
          │
          ▼
Replay Context
          │
          ▼
Federation Context
          │
          ▼
AI Context
          │
          ▼
Risk Context
          │
          ▼
Context Merge
          │
          ▼
Emit Enriched Signal

The engine is intentionally modular so each enrichment stage may evolve independently while maintaining deterministic outputs.

11. Context Resolution Algorithm

Each incoming signal receives a progressively richer execution context.

Pseudo workflow:

ResolveIdentity()

ResolveSchemas()

ResolveOrganization()

ResolveLineage()

ResolveReplay()

ResolveGovernance()

ResolveFederation()

ResolveAI()

ResolveRisk()

MergeContexts()

ValidateContext()

Publish()

Every stage either:

enriches the signal
produces an explainable warning
aborts processing

depending upon configured policy.

12. Runtime Algorithm

function enrich(signal):

    identity = IdentityResolver.resolve(signal.eco_number)

    schemas = SchemaResolver.load(signal.metric_type)

    organization = OrganizationResolver.load(identity)

    lineage = LineageResolver.resolve(signal)

    governance = GovernanceResolver.load(identity)

    replay = ReplayResolver.lookup(signal)

    federation = FederationResolver.lookup(identity)

    ai = AIResolver.recommend(signal)

    risk = RiskResolver.evaluate(signal)

    context = MergeContext(
        signal,
        identity,
        organization,
        schemas,
        lineage,
        governance,
        replay,
        federation,
        ai,
        risk
    )

    Validate(context)

    return context

The algorithm must remain deterministic.

Identical inputs shall always produce identical enrichment outputs.

13. Identity Resolution

Identity resolution is always executed first.

Inputs include:

ECO Number
DID
Controller URI
Federation Node
Organization Identifier

The engine retrieves:

Property	Source
ECO metadata	ZAR
DID Document	EGFS
Federation Node	EGFS Registry
Organization status	FAGF
Trust profile	AII

Failure to resolve identity immediately terminates enrichment.

14. Organization Context Resolution

Once identity has been established, organizational metadata is attached.

Retrieved information includes:

Metadata	Source
Organization type	ZAR
Industry	NACE Registry
Country	Country Registry
Federation Region	EGFS
Assurance Tier	FAGF
Regulatory Scope	Governance Registry
Tenant configuration	ZAR

Example:

{
  "organization": {
    "eco_number": "ECO-A123",
    "type": "Enterprise",
    "region": "EU",
    "nace":"C25.11",
    "assurance_tier":"Tier-2"
  }
}

15. Schema Resolution

The engine resolves the canonical schema required for the incoming signal.

Primary source:

SSSR

Lookup sequence:

Metric Type

↓

Schema Registry

↓

Latest Approved Schema

↓

Version Compatibility

↓

Schema Cache

↓

Runtime Attachment

Returned metadata includes:

CSI
schema version
validation profile
semantic mappings
ontology references

16. Lineage Resolution

Operational lineage is retrieved from USO.

Additional verification anchors are retrieved from DAL.

Information attached includes:

Item	Source
Event lineage	USO
Processing history	USO
Replay references	AAE
DAL anchors	DAL
Verification proofs	DAL

Example:

{
  "lineage": {
    "uso_id":"USO-9981",
    "dal_anchor":"DAL-782991",
    "parent_events":18,
    "verified":true
  }
}

17. Governance Resolution

Applicable governance rules are loaded dynamically.

Sources:

FAGF
OARM
Policy Registry

Retrieved:

active policy bundle
regulatory profile
organization overrides
reviewer requirements
trust thresholds

The engine never embeds governance logic.

It only retrieves active policies.

18. Replay Context Resolution

Historical replay information is retrieved from AAE.

Information includes:

Field	Description
replay_count	Previous replay executions
replay_success	Replay verification rate
replay_variance	Historical drift
confidence	Replay confidence
last_replay	Timestamp

Replay information contributes to downstream trust scoring.

19. Federation Context Resolution

Federation metadata is retrieved through EGFS.

Attached metadata includes:

federation node
regional trust profile
synchronization status
node health
certificate chain
policy version

Example:

{
    "federation":{
        "region":"EU",
        "node":"EU-3",
        "status":"healthy",
        "sync_state":"current"
    }
}

20. AI Context Resolution

Context recommendations are requested from DSAIL.

Returned recommendations are advisory.

Examples:

{
    "ai":{
        "risk_score":0.19,
        "confidence":0.94,
        "recommendation":"Increase replay frequency"
    }
}

The engine never allows AI to modify facts.

AI only augments context.

21. Risk Context Resolution

Risk metadata originates from:

RIF
DSAIL
TrustGate history

Attached metadata includes:

entity risk
supplier risk
geopolitical risk
anomaly score
fraud indicators

Risk information is later consumed by Trust Scoring.

22. Context Merge Strategy

After every enrichment stage completes successfully, all context objects are merged.

Merge priority:

Incoming Signal

↓

Identity

↓

Organization

↓

Schema

↓

Lineage

↓

Governance

↓

Replay

↓

Federation

↓

AI

↓

Risk

Conflicts are never silently overwritten.

Conflicts produce explicit enrichment events.

23. Context Completeness Score

The engine computes an internal completeness indicator.

This metric is not a trust score.

It measures enrichment completeness.

The score is calculated as:

ContextCompleteness= \frac{ ResolvedContextObjects } { RequiredContextObjects }

Required objects include:

identity
schema
organization
governance
lineage

Optional objects include:

AI
federation
replay
risk

This metric supports operational monitoring but is never exposed as a public TrustGate score.

24. Failure Handling

Each enrichment stage defines a severity level.

Failure	Action
Missing identity	Abort
Missing schema	Abort
Missing lineage	Warning
Missing replay	Continue
Missing federation	Continue
Missing AI	Continue
Missing governance	Abort

Failures produce immutable enrichment events recorded within USO.

25. Runtime Characteristics

Characteristic	Target
Average latency	< 60 ms
P95 latency	< 150 ms
Maximum external lookups	10
Maximum retries	3
Cache TTL	10 minutes
Parallel lookup support	Yes
Replay deterministic	Yes
Stateless execution	Yes

The engine is designed for horizontal scaling and may execute concurrently across multiple TrustGate worker instances while maintaining deterministic outputs.

26. Canonical Input Schema

The Context Enrichment Engine accepts only normalized TrustGate runtime objects.

The input shall already have successfully completed:

Signal Parsing
Signal Normalization

No schema validation beyond structural verification is performed prior to enrichment.

Example:

{
  "signal_id": "SIG-2026-00001472",
  "eco_number": "ECO-A123",
  "metric_type": "energy.total_consumption",
  "metric_value": 12843.2,
  "unit": "kWh",
  "period": "2026-Q1",
  "source_system": "SAP-S4",
  "received_at": "2026-06-25T10:41:13Z",
  "normalization_version": "1.0.0"
}

27. Canonical Enriched Output

After enrichment the signal becomes the canonical TrustGate runtime object.

Example:

{
  "signal_id": "SIG-2026-00001472",

  "identity": {
    "eco_number": "ECO-A123",
    "did": "did:zayaz:ECO-A123",
    "organization_type": "Enterprise"
  },

  "metric": {
    "metric_type": "energy.total_consumption",
    "metric_value": 12843.2,
    "unit": "kWh",
    "period": "2026-Q1"
  },

  "organization": {
    "nace": "C25.11",
    "country": "DK",
    "assurance_tier": "Tier-2"
  },

  "schema": {
    "csi": "comp.AIIL.INPUT.TRUST-SCORE.1_0",
    "version": "1.2.0"
  },

  "lineage": {
    "uso_id": "USO-98122",
    "dal_anchor": "DAL-991223",
    "verified": true
  },

  "governance": {
    "policy_bundle": "FAGF-2026.2"
  },

  "replay": {
    "confidence": 0.96,
    "replay_count": 7
  },

  "federation": {
    "region": "EU",
    "node": "EU-3"
  },

  "ai": {
    "confidence": 0.93,
    "recommendation": "Increase replay frequency"
  }
}

28. Context Object Model

The engine produces nine logical context objects.

Context Object	Mandatory	Source
Identity	✓	EGFS / ZAR
Organization	✓	ZAR
Schema	✓	SSSR
Lineage	✓	USO + DAL
Governance	✓	FAGF
Replay	Optional	AAE
Federation	Optional	EGFS
AI	Optional	DSAIL
Risk	Optional	RIF

Mandatory objects are required for successful completion.

29. Runtime Lookup Matrix

The engine performs several independent lookups.

Lookup	Primary Source	Secondary Source	Cache
Identity	EGFS Registry	ZAR	Yes
Organization	ZAR	USO	Yes
Schema	SSSR	Cache	Yes
Lineage	USO	DAL	No
Governance	FAGF	OARM	Yes
Replay	AAE	DAL	No
Federation	EGFS	Registry Cache	Yes
AI	DSAIL	—	No
Risk	RIF	DSAIL	No

Lookups may execute in parallel where dependencies permit.

30. CSI Contracts

The engine consumes and produces canonical CSIs.

Input CSIs

CSI	Purpose
comp.TG.INPUT.NORMALIZED-SIGNAL.1_0	Canonical normalized TrustGate signal
comp.USO.LINEAGE.EVENT.1_0	Operational lineage
comp.DAL.ANCHOR.PROOF.1_0	Ledger verification
comp.SSSR.SCHEMA.REGISTRY.1_0	Schema metadata

Output CSIs

CSI	Purpose
comp.TG.OUTPUT.ENRICHED-SIGNAL.1_0	Canonical enriched TrustGate signal
comp.TG.EVENT.ENRICHMENT.1_0	Enrichment audit event
comp.TG.EVENT.CONTEXT-COMPLETE.1_0	Context completeness event

31. CMI Registration

The engine is registered in ZAR.

Property	Value
CMI	vera.TG-ENRICH.ENGINE.CONTEXT.1_0_0
MEID	MEID_SIGNAL_ENRICH_CONTEXT
Engine Type	ENRICH
Runtime	TrustGate
Registry	ZAR

32. Context Merge Rules

Context merging follows deterministic precedence.

Priority	Context
1	Incoming Signal
2	Identity
3	Organization
4	Schema
5	Lineage
6	Governance
7	Replay
8	Federation
9	AI
10	Risk

No downstream context may overwrite upstream facts.

Conflicting values generate explicit enrichment conflict events.

33. Enrichment Event Model

Every enrichment stage emits immutable events.

Example:

{
  "event_type": "context_enriched",
  "signal_id": "SIG-2026-00001472",
  "stage": "Governance",
  "status": "success",
  "duration_ms": 14,
  "source": "FAGF",
  "timestamp": "2026-06-25T10:41:15Z"
}

Events become part of the USO operational history.

34. Context Completeness Event

After merging, the engine emits a completeness summary.

Example:

{
  "signal_id": "SIG-2026-00001472",
  "resolved_objects": 9,
  "required_objects": 9,
  "optional_objects": 3,
  "context_completeness": 1.00,
  "status": "complete"
}

The Context Completeness metric is an operational KPI and shall never be interpreted as a trust score.

35. API Specification

Endpoint

POST /api/trustgate/MEID_SIGNAL_ENRICH_CONTEXT

Request Headers

Header	Required
Authorization	✓
X-ECO-Number	✓
X-Request-ID	✓
Content-Type	✓

Request Body

{
  "signal": { ... }
}

Successful Response

HTTP/1.1 200 OK

{
  "status": "success",
  "signal_id": "SIG-2026-00001472",
  "context_status": "complete",
  "processing_time_ms": 41
}

Failure Response

HTTP/1.1 422 Unprocessable Entity

{
  "status": "failed",
  "error_code": "IDENTITY_NOT_FOUND",
  "message": "Unable to resolve ECO Number."
}

36. Engine Metrics

The engine publishes operational metrics to VIZZ and the Assurance Analytics Engine (AAE).

Metric	Description
Enrichment Throughput	Signals processed per second
Lookup Latency	Average lookup time by dependency
Cache Hit Rate	Percentage of cache hits
Context Completeness	Average completeness ratio
Lookup Failures	Failed external lookups
Governance Version Drift	Outdated policy bundle usage
Federation Availability	Federation lookup success
Average Processing Time	End-to-end execution latency

These metrics provide operational visibility and feed capacity planning, anomaly detection, and runtime optimization dashboards.

37. Replay Integration

The Context Enrichment Engine is fully replay-aware and participates in the TrustGate replay framework coordinated by the Autonomous Assurance Engine (AAE).

The engine does not perform replay verification itself but enriches signals with replay metadata that downstream engines use during trust scoring, assurance, and certification.

Replay Inputs

Source	Purpose
AAE	Replay history
DAL	Previous ledger anchors
USO	Historical execution lineage
TrustGate	Previous trust decisions

Replay Metadata

Example:

{
  "replay": {
    "replay_id": "RPL-2026-000842",
    "replay_count": 7,
    "last_replay_at": "2026-06-21T14:22:10Z",
    "verification_rate": 0.986,
    "confidence": 0.97,
    "variance": 0.011,
    "verified": true
  }
}

Replay Resolution Strategy

The replay subsystem follows this lookup order:

Signal

↓

USO Lineage

↓

AAE Replay Registry

↓

DAL Anchors

↓

Historical Trust Scores

↓

Replay Context

Replay data is immutable.

Historical replay records are never modified.

38. Federation Integration

The engine operates transparently across the entire ZAYAZ Federation.

Identity and federation metadata are resolved through EGFS.

The engine is federation-neutral.

It does not distinguish between:

organizations
suppliers
regulators
auditors
governments

Every participant is represented by an ECO Number and a DID.

Federation Services Used

Service	Purpose
Federation Registry	Resolve organization
DID Registry	Resolve identity
Policy Distribution	Retrieve governance
Federation Health	Node availability
Federation Routing	Cross-region routing

Federation Context Example

{
  "federation": {
    "eco_number": "ECO-A123",
    "did": "did:zayaz:ECO-A123",
    "region": "EU",
    "node": "EU-3",
    "policy_version": "FAGF-2026.2",
    "sync_state": "current",
    "certificate_status": "valid"
  }
}

39. AI Integration

The Context Enrichment Engine interacts with DSAIL exclusively through advisory interfaces.

Artificial Intelligence never modifies factual signal data.

AI may only recommend:

replay priorities
anomaly indicators
enrichment recommendations
confidence estimates
risk indicators

All recommendations are stored separately from factual metadata.

AI Recommendation Example

{
  "ai": {
    "model": "vera.DSAIL.MODEL.CONTEXT.2_0_0",
    "confidence": 0.95,
    "recommendation": "Increase replay frequency",
    "reason": "Higher than expected variance",
    "explainability_score": 0.93
  }
}

AI Safety Rules

The engine enforces the following principles:

Rule	Description
AI cannot overwrite facts	Mandatory
AI recommendations are explainable	Mandatory
AI confidence must be recorded	Mandatory
Human override supported	Mandatory
AI model version recorded	Mandatory
Recommendation provenance retained	Mandatory

All AI outputs become lineage events within USO.

40. DAL Integration

The engine enriches signals with Distributed Assurance Ledger (DAL) references.

No new anchors are created.

Instead, the engine resolves:

previous anchors
parent anchors
verification proofs
replay anchors

Example:

{
  "dal": {
    "anchor_id": "DAL-991223",
    "parent_anchor": "DAL-991120",
    "verification_status": "verified",
    "proof_hash": "b97dca62..."
  }
}

41. USO Integration

The Universal Sustainability Object (USO) provides the operational lineage required for enrichment.

Retrieved metadata includes:

Metadata	Description
Event history	Complete execution chain
Parent object	Previous operational object
Processing stage	Current lifecycle stage
Lineage depth	Number of ancestors
Replay references	Historical replay events

USO provides the canonical operational history.

42. Security Model

The Context Enrichment Engine processes trusted internal runtime data only.

External requests must pass TrustGate authentication before enrichment.

Authentication

Supported mechanisms:

OAuth2
Mutual TLS
Federation certificates
JWT
API Keys (internal)

Authorization

Authorization is evaluated using:

ECO identity
Federation role
Organization permissions
Runtime policy bundle

Encryption

Layer	Standard
Transport	TLS 1.3
Internal gRPC	mTLS
Stored secrets	AWS KMS
DAL Anchors	SHA-256 + Ed25519
AI Models	Signed ZAR packages

43. Audit Logging

Every enrichment stage generates immutable audit records.

Example:

{
  "event_type": "context_lookup",
  "resolver": "SchemaResolver",
  "source": "SSSR",
  "duration_ms": 18,
  "status": "success",
  "timestamp": "2026-06-25T10:42:19Z"
}

Audit events become part of the operational lineage.

44. Observability

Operational metrics are exported to:

VIZZ
AAE
TrustGate Dashboard
CloudWatch
Prometheus

Runtime Metrics

Metric	Description
Signals/sec	Processing throughput
Lookup latency	External dependency latency
Cache hit ratio	Cache efficiency
Replay lookup latency	Replay subsystem performance
Context completeness	Average completeness score
Federation lookup latency	EGFS performance
DAL lookup latency	Ledger access time
Failure rate	Processing failures

45. Health Checks

The engine exposes runtime health endpoints.

Endpoint	Purpose
`/health`	Liveness
`/ready`	Readiness
`/metrics`	Prometheus metrics
`/version`	Runtime version
`/status`	Dependency status

46. Deployment Model

The Context Enrichment Engine is stateless.

Multiple instances may execute concurrently.

Deployment targets include:

Kubernetes
ECS
EKS
Local development
Integration test environments

Caching is externalized.

No runtime state is stored locally.

47. Failure Recovery

The engine supports graceful degradation.

Dependency	Behaviour
SSSR unavailable	Retry → Fail
ZAR unavailable	Retry → Fail
EGFS unavailable	Cached lookup
AAE unavailable	Continue without replay
DSAIL unavailable	Continue without AI
RIF unavailable	Continue without risk
DAL unavailable	Retry → Cached proof

Only failures affecting mandatory context objects terminate processing.

48. Compliance

The Context Enrichment Engine contributes to compliance with:

Framework	Purpose
CSRD	Sustainability reporting
ESRS	ESG disclosures
ISSB	Sustainability disclosures
IFRS S1	Governance
IFRS S2	Climate disclosures
ISO 27001	Information security
ISO 14064-1	GHG assurance
EU AI Act	Explainable AI
W3C DID	Decentralized identities
W3C Verifiable Credentials	Digital assurance credentials

49. Engine Dependencies

Upstream Components

TrustGate Parser
TrustGate Normalizer
USO
ZAR
EGFS

Downstream Components

Trust Scoring Engine
Risk Engine
AAE
DAL
AII
VIZZ

50. Summary

The TrustGate Context Enrichment Engine transforms normalized runtime signals into fully contextualized operational objects by attaching identity, governance, schema, lineage, replay, federation, AI, and risk metadata.

The engine serves as the canonical enrichment layer within the TrustGate runtime pipeline and ensures that all downstream trust scoring, verification, assurance, replay, federation synchronization, and audit processes operate on semantically complete, cryptographically traceable, and governance-aware data.

GitHub Repo Request for Change (RFC)

1. Purpose​

2. Position within TrustGate Runtime​

3. Responsibilities​

4. Runtime Architecture​

5. Runtime Lifecycle​

6. External Dependencies​

7. Engine Registration​

8. Design Principles​

9. Downstream Consumers​

10. Runtime Processing Pipeline​

11. Context Resolution Algorithm

12. Runtime Algorithm

13. Identity Resolution

14. Organization Context Resolution

15. Schema Resolution

16. Lineage Resolution

17. Governance Resolution

18. Replay Context Resolution

19. Federation Context Resolution

20. AI Context Resolution

21. Risk Context Resolution

22. Context Merge Strategy

23. Context Completeness Score

24. Failure Handling

25. Runtime Characteristics

26. Canonical Input Schema

27. Canonical Enriched Output

28. Context Object Model

29. Runtime Lookup Matrix

30. CSI Contracts

Input CSIs​

Output CSIs​

31. CMI Registration

32. Context Merge Rules

33. Enrichment Event Model

34. Context Completeness Event

35. API Specification

Endpoint​

Request Headers​

Request Body​

Successful Response​

Failure Response​

36. Engine Metrics

37. Replay Integration

Replay Inputs​

Replay Metadata​

Replay Resolution Strategy​

38. Federation Integration

Federation Services Used​

Federation Context Example​

39. AI Integration

AI Recommendation Example​

AI Safety Rules​

40. DAL Integration

41. USO Integration

42. Security Model

Authentication​

Authorization​

Encryption​

43. Audit Logging

44. Observability

Runtime Metrics​

45. Health Checks

46. Deployment Model

47. Failure Recovery

48. Compliance

49. Engine Dependencies

Upstream Components​

Downstream Components​

50. Summary

1. Purpose

2. Position within TrustGate Runtime

3. Responsibilities

4. Runtime Architecture

5. Runtime Lifecycle

6. External Dependencies

7. Engine Registration

8. Design Principles

9. Downstream Consumers

10. Runtime Processing Pipeline

Input CSIs

Output CSIs

Endpoint

Request Headers

Request Body

Successful Response

Failure Response

Replay Inputs

Replay Metadata

Replay Resolution Strategy

Federation Services Used

Federation Context Example

AI Recommendation Example

AI Safety Rules

Authentication

Authorization

Encryption

Runtime Metrics

Upstream Components

Downstream Components