Digital Twin Sync Workflow

Intent

Synchronize a digital twin with real-time signals and keep it grounded, safe, and auditable. This workflow maintains alignment between the real world and its digital representation.

This workflow is designed to model both real and digital systems using the canonical world-state schema:

• reference/​world_state_schema.yaml

Success criteria:

• Twin snapshot updated with latest signals
• All drift and anomalies detected and documented
• Risk assessed and forecasted with evidence
• Actions executed only after checkpoint and approval
• Complete audit trail with provenance
• Rollback available if verification fails

Compatible schemas:

• reference/​world_state_schema.yaml
• reference/​event_schema.yaml
• reference/​workflow_catalog.yaml

Inputs

Preconditions (hard gates)

1. Policy constraints must exist (/​constrain)
2. Checkpoint before mutation (/​checkpoint)
3. Any external side effects require explicit approval (do not send without approval)

Procedure

1. Ensure baseline exists: If no twin snapshot exists, start with /​world-model-workflow

Then execute the sync loop:

1. Invoke /​receive → store receive_out

   • Ingest signals from configured sources

2. Invoke /​transform to normalize to canonical events → transform_out

   • Convert raw signals to event schema format

3. Invoke /​integrate to merge events with prior twin snapshot → integrate_out

   • Combine new events with existing state

4. Invoke /​identity-resolution → identity_resolution_out

   • Resolve entity references across sources

5. Invoke /​world-state producing canonical snapshot → world_state_out

   • Generate updated twin representation

6. Invoke /​state-transition apply rules → state_transition_out

   • Apply business rules and state machine logic

7. Invoke /​detect-anomaly drift detection → detect_anomaly_out

   • Identify deviations from expected behavior

8. Invoke /​estimate-risk risk estimate → estimate_risk_out

   • Assess current risk based on anomalies

9. Invoke /​forecast-risk risk forecast → forecast_risk_out

   • Project future risk trajectory

10. Invoke /​plan remediation plan + verification criteria + rollback plan → plan_out

    • Create action plan if intervention needed

11. Invoke /​constrain enforce policy constraints → constrain_out

    • Validate plan against policy limits

12. Invoke /​checkpoint create mutation gate marker → checkpoint_out

    • Establish restore point before action

13. Invoke /​act-plan execute if safe/​approved → act_plan_out

    • Execute remediation actions

14. Invoke /​verify PASS/​FAIL → verify_out

    • Confirm actions achieved intended outcome

15. Invoke /​audit provenance + tool log → audit_out

    • Record complete audit trail

16. If FAIL or side effects → /​rollback → rollback_out

    • Restore previous state if needed

17. Invoke /​summarize decision-ready report → summarize_out

    • Generate executive summary

Output Contract

Return a structured object:

workflow_id: string  # Unique sync execution ID
world_id: string  # Digital twin identifier
sync_timestamp: string  # ISO timestamp of sync
status: synced | drift_detected | action_taken | rolled_back | failed
twin_snapshot:
  version: string
  state: object  # Canonical world state
  hash: string  # Integrity hash
  evidence_anchors: array[string]
drift_report:
  anomalies_detected: integer
  severity: low | medium | high | critical
  triggers: array[string]
  evidence_anchors: array[string]
risk_assessment:
  current_risk: number  # 0.0-1.0
  forecasted_risk: number  # 0.0-1.0
  risk_factors: array[string]
  evidence_anchors: array[string]
actions:
  executed: boolean
  plan_summary: string
  changes: array[string]
  safety_gates_passed: boolean
  evidence_anchors: array[string]
verification:
  result: PASS | FAIL | SKIPPED
  criteria_met: array[string]
  evidence_anchors: array[string]
audit:
  log_path: string
  provenance_chain: array[string]
  evidence_anchors: array[string]
rollback:
  available: boolean
  executed: boolean
  restore_point: string | null
  command: string | null
next_sync:
  recommended_interval: string  # e.g., "5m", "1h"
  triggers: array[string]  # Conditions for immediate resync
confidence: number  # 0.0-1.0
evidence_anchors: array[string]
assumptions: array[string]

Field Definitions

Examples

Example 1: IoT Sensor Sync with Anomaly Detection

Input:

sources:
  - type: mqtt
    endpoint: "mqtt://sensors.example.com/​floor-3"
  - type: api
    endpoint: "https://building.api/​hvac/​status"
world_id: "building-floor-3-twin"
constraints:
  max_drift_threshold: 0.15
  require_approval_above_risk: 0.7
sync_mode: incremental

Output:

workflow_id: "sync_20240115_160000_floor3"
world_id: "building-floor-3-twin"
sync_timestamp: "2024-01-15T16:00:00Z"
status: drift_detected
twin_snapshot:
  version: "v47"
  state:
    entities:
      - id: "hvac-unit-3a"
        type: "hvac_controller"
        temperature: 23.5
        setpoint: 22.0
        status: "cooling"
      - id: "sensor-temp-301"
        type: "temperature_sensor"
        reading: 24.1
        last_updated: "2024-01-15T15:59:45Z"
    relationships:
      - subject: "hvac-unit-3a"
        predicate: "controls"
        object: "zone-3a"
  hash: "sha256:abc123def456..."
  evidence_anchors:
    - "tool:mqtt:sensors.example.com/​floor-3"
    - "tool:api:building.api/​hvac/​status"
drift_report:
  anomalies_detected: 1
  severity: medium
  triggers:
    - "Temperature 1.5°C above setpoint for >10 minutes"
  evidence_anchors:
    - "file:state/​floor-3-twin-v46.yaml:temperature_history"
    - "tool:detect-anomaly:threshold_breach"
risk_assessment:
  current_risk: 0.35
  forecasted_risk: 0.45
  risk_factors:
    - "HVAC may be undersized for current load"
    - "Trending toward comfort threshold breach"
  evidence_anchors:
    - "tool:estimate-risk:hvac_capacity"
    - "tool:forecast-risk:temperature_trend"
actions:
  executed: false
  plan_summary: "Monitor for 15 more minutes before intervention"
  changes: []
  safety_gates_passed: true
  evidence_anchors:
    - "tool:plan:remediation_decision"
verification:
  result: SKIPPED
  criteria_met: []
  evidence_anchors: []
audit:
  log_path: ".claude/​audit/​sync_20240115_160000_floor3.log"
  provenance_chain:
    - "mqtt://sensors.example.com → receive"
    - "receive → transform"
    - "transform → integrate"
    - "integrate → world-state"
  evidence_anchors:
    - "file:.claude/​audit/​sync_20240115_160000_floor3.log"
rollback:
  available: false
  executed: false
  restore_point: null
  command: null
next_sync:
  recommended_interval: "5m"
  triggers:
    - "Temperature exceeds 25°C"
    - "HVAC status changes"
confidence: 0.88
evidence_anchors:
  - "tool:mqtt:sensors.example.com/​floor-3"
  - "tool:api:building.api/​hvac/​status"
  - "tool:detect-anomaly:threshold_breach"
  - "file:.claude/​audit/​sync_20240115_160000_floor3.log"
assumptions:
  - "Sensor readings are accurate within ±0.1°C"
  - "MQTT connection is reliable"
  - "Building API returns real-time status"

Evidence pattern: Multi-source signal ingestion, anomaly detection against historical baseline, risk forecasting with trend analysis.

Verification

• Source Ingestion: All configured sources successfully polled
• Transform Success: Events conform to canonical schema
• Identity Resolved: No unresolved entity references
• Drift Detection: Anomaly check completed with evidence
• Risk Assessment: Both current and forecast risk computed
• Policy Compliance: Constraints checked before any action
• Checkpoint Valid: Restore point exists if actions taken
• Audit Complete: Provenance chain documented
• Next Sync Scheduled: Recommended interval provided

Verification tools: Web (for API checks), Bash (for MQTT), Read (for state files)

Safety Constraints

• mutation: true
• requires_checkpoint: true
• requires_approval: true (for actions above risk threshold)
• risk: high

Capability-specific rules:

• STOP on low confidence from any perception step
• NEVER execute mutation without checkpoint
• NEVER emit external side effects without explicit approval
• Validate all source data before integration
• Preserve previous snapshot for rollback
• Rate-limit sync frequency to prevent thrashing

Composition Patterns

Commonly follows:

• world-model-workflow - Initial twin creation before first sync
• receive - When triggered by external event

Commonly precedes:

• summarize - Create executive report after sync
• send - Notify stakeholders of drift or actions
• Self (recursive) - Next sync iteration

Anti-patterns:

• Never sync without prior world model established
• Never skip anomaly detection to proceed directly to action
• Never execute actions without policy constraint check
• Never delete audit logs before retention period

Workflow references:

• See reference/​workflow_catalog.yaml#digital-twin-sync-loop for step definitions
• See reference/​world_state_schema.yaml for canonical state format
• See reference/​composition_patterns.md#checkpoint-act-verify-rollback for CAVR pattern