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Author	SHA1	Message	Date
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
Bernt	c6e14c5928	ADR-012: Five Engines Platform Architecture + Economic Engine - LANDVEX_PLATFORM_ARCHITECTURE.md: Five Engines (Reality, Knowledge, Decision, Mission, Economic) - Credit: first-class economic object with types (mission, validation, training, priority, emergency) - IntelligenceLedger: tracks value creation separate from financial accounting - KnowledgeGap: missing information that drives missions - Hotspot: composite score for mission generation - Contradiction: conflicting information as opportunity Key principle: Every component answers 'What value is created here? Who pays for it?' Next: PR-005A — Minimal Mission Import UI for MVP-0	2026-07-02 16:05:56 +00:00
Bernt	754c89506b	ADR-011: Four-Layer Data Architecture — Raw Archive → Knowledge → Ontology → Decision - Layer 1: ArchiveArtifact — immutable original with retention policy - Layer 2: KnowledgeArtifact — extracted knowledge (observations, segmentations, feature vectors, relations) - Layer 3: Knowledge Graph / Ontology (documented, not implemented) - Layer 4: Decision Intelligence (existing DecisionCase) - DataLifecycle: tracks every step with artifact lineage - Key principle: AI models trained on curated datasets, not whole archive - Ontology answers 'what does it mean in our domain?' Long-term goal: Every observation converted once to structured knowledge, reused infinitely for analysis, decisions, training. Next: PR-005A — Minimal Mission Import UI for MVP-0	2026-07-02 16:03:39 +00:00

Author

SHA1

Message

Date

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

Bernt

c6e14c5928

ADR-012: Five Engines Platform Architecture + Economic Engine

- LANDVEX_PLATFORM_ARCHITECTURE.md: Five Engines (Reality, Knowledge,
  Decision, Mission, Economic)
- Credit: first-class economic object with types (mission, validation,
  training, priority, emergency)
- IntelligenceLedger: tracks value creation separate from financial accounting
- KnowledgeGap: missing information that drives missions
- Hotspot: composite score for mission generation
- Contradiction: conflicting information as opportunity

Key principle: Every component answers 'What value is created here?
Who pays for it?'

Next: PR-005A — Minimal Mission Import UI for MVP-0

2026-07-02 16:05:56 +00:00

Bernt

754c89506b

ADR-011: Four-Layer Data Architecture — Raw Archive → Knowledge → Ontology → Decision

- Layer 1: ArchiveArtifact — immutable original with retention policy
- Layer 2: KnowledgeArtifact — extracted knowledge (observations,
  segmentations, feature vectors, relations)
- Layer 3: Knowledge Graph / Ontology (documented, not implemented)
- Layer 4: Decision Intelligence (existing DecisionCase)
- DataLifecycle: tracks every step with artifact lineage
- Key principle: AI models trained on curated datasets, not whole archive
- Ontology answers 'what does it mean in our domain?'

Long-term goal: Every observation converted once to structured
knowledge, reused infinitely for analysis, decisions, training.

Next: PR-005A — Minimal Mission Import UI for MVP-0

2026-07-02 16:03:39 +00:00

3 Commits