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Bernt eae6c58d66 ADR-013: Engine Architecture v1.0 — Six Engines + Orchestrator
- Reality Engine: capture reality → observations
- Knowledge Engine: understand meaning → evidence, findings
- Decision Engine: make actionable → decisions, recommendations
- Mission Engine: determine what to collect → missions, gaps
- Economic Engine: manage budgets → credits, ROI
- Learning Engine: continuously improve → better models
- Platform Orchestrator: coordinates via events

Key principles:
- Each engine independently versioned
- Engines never call each other directly
- All communication via events
- Dashboard shows results, not engines
- Intelligence Lab tests engines, not platform

Next: PR-005A — Mission Import UI for MVP-0
2026-07-02 16:11:58 +00:00

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LandveX Engine Architecture v1.0

Platform Overview

LandveX is not a collection of services. It is a platform of autonomous engines with clear responsibilities.

Reality Engine
  ↓
Knowledge Engine
  ↓
Decision Engine
  ↓
Mission Engine
  ↓
Economic Engine
  ↓
Learning Engine

Platform Orchestrator coordinates engines via events. Not AI. Not Dashboard. Just orchestration.


Engine 1 — Reality Engine

Responsibility: Transform reality into verified observations.

Input:

  • quiXzoom (phone)
  • Drones
  • Cameras
  • Video
  • GIS
  • Sensors

Output:

  • Observation
  • Artifact
  • Metadata

Domain objects created:

  • FieldSession
  • Mission
  • Observation
  • ArchiveArtifact

Events produced:

  • ObservationCreated
  • MissionCompleted
  • ArtifactRegistered

KPIs:

  • Observations per hour
  • Upload success rate
  • Metadata completeness

Engine 2 — Knowledge Engine

Responsibility: Understand what observations mean.

Input:

  • Observation
  • ArchiveArtifact

Output:

  • Evidence
  • Finding
  • KnowledgeArtifact
  • Ontology entries
  • Taxonomy classifications
  • Object relations
  • Knowledge Graph updates

Domain objects created:

  • Evidence
  • Finding
  • KnowledgeArtifact

Events consumed:

  • ObservationCreated

Events produced:

  • EvidenceCreated
  • FindingCreated
  • KnowledgeExtracted

KPIs:

  • Knowledge extraction accuracy
  • Ontology coverage
  • Taxonomy completeness

Engine 3 — Decision Engine

Responsibility: Make knowledge actionable.

Input:

  • Finding
  • Evidence
  • Knowledge Graph

Output:

  • Decision
  • Priority
  • Confidence
  • Consequence
  • Recommendation

Domain objects created:

  • DecisionCase
  • Decision
  • Review

Events consumed:

  • FindingCreated

Events produced:

  • DecisionCreated
  • DecisionApproved
  • DecisionRejected

KPIs:

  • Decision accuracy
  • Time to decision
  • Review completion rate

Engine 4 — Mission Engine

Responsibility: Determine what data to collect next.

Input:

  • KnowledgeGap
  • Hotspot
  • Contradiction
  • Customer requests
  • Budget
  • SLA

Output:

  • Mission
  • Reward
  • Priority
  • Coverage

Domain objects created:

  • Mission
  • KnowledgeGap
  • Hotspot
  • Contradiction

Events consumed:

  • KnowledgeGapIdentified
  • ContradictionDetected

Events produced:

  • MissionCreated
  • MissionAssigned
  • MissionCompleted

KPIs:

  • Knowledge gap closure rate
  • Mission success rate
  • Coverage improvement

Engine 5 — Economic Engine

Responsibility: Manage budgets, credits, and ROI.

Input:

  • Budget
  • Mission
  • VerifiedDelivery

Output:

  • Credit
  • Settlement
  • ROI
  • RewardPricing
  • CostAllocation

Domain objects created:

  • Credit
  • CreditBudget
  • IntelligenceLedgerEntry

Events consumed:

  • MissionCompleted
  • DecisionApproved

Events produced:

  • CreditsAllocated
  • PaymentSettled
  • RoiCalculated

KPIs:

  • Cost per decision
  • Knowledge return per credit
  • Budget utilization

Engine 6 — Learning Engine

Responsibility: Continuously improve all engines.

Input:

  • Outcome
  • Decision
  • Reality (ground truth)

Output:

  • Better models
  • Better rules
  • Better missions
  • Better pricing

Events consumed:

  • DecisionApproved
  • MissionCompleted
  • OutcomeVerified

Events produced:

  • ModelUpdated
  • RulesRefined
  • PricingAdjusted

KPIs:

  • Model improvement rate
  • Prediction accuracy gain
  • Cost reduction

Platform Orchestrator

Responsibility: Coordinate engines via events.

Not AI. Not Dashboard. Just orchestration.

Example flow:

ObservationCreated
  ↓
Knowledge Engine (extract knowledge)
  ↓
Decision Engine (create decision)
  ↓
Mission Engine (check for gaps)
  ↓
Economic Engine (calculate ROI)

Rules:

  • Engines never call each other directly
  • All communication via events
  • Orchestrator decides which engine runs next
  • Each engine is independently versioned

Dashboard

Dashboard never shows engines.

Dashboard shows results:

AREA
  Score
  ↓
  Top Decisions
  ↓
  Hotspots
  ↓
  Business Impact
  ↓
  Recommended Actions

Intelligence Lab

Intelligence Lab is the development environment for engines.

Test:

  • Knowledge Engine
  • Decision Engine
  • Learning Engine

Not:

  • Whole platform
  • Production data
  • End-to-end flows

Engine Contracts

Each engine MUST define:

Contract Description
Responsibility What this engine does
Input What it consumes
Output What it produces
Domain Objects What it can create/modify
Events In Events it listens to
Events Out Events it produces
KPIs How quality is measured

Development Principles

  1. Each engine independently versioned
  2. Engines never call each other directly
  3. All communication via events
  4. Orchestrator decides flow
  5. Dashboard shows results, not engines
  6. Intelligence Lab tests engines, not platform

  • ADR-011: Four-Layer Data Architecture
  • ADR-012: Economic Engine
  • LANDVEX_PLATFORM_ARCHITECTURE.md