Decision Lineage Protocol

Architecture Specification · v2.0.0 · March 19, 2026

Organizations produce decisions continuously, yet the reasoning underlying those decisions is rarely captured in a structured, queryable form. The Decision Lineage Protocol addresses this infrastructure gap by providing an AI-native governance substrate — a formal architecture that captures decisions as state transformations with full lineage, enforced by behavioral invariants and typed by epistemic provenance.

DLP is built on nineteen irreducible primitives organized across five tiers and governed by ten behavioral invariants (B1–B10). The architecture derives from a five-step chain grounded in established theory: Conant-Ashby's theorem that governance must be a model, Ashby's law of requisite variety, Beer's Viable System Model, LeCun's world model architecture, and a formal proof that log-loss optimization breaks the symmetries governance must preserve — meaning governance invariances cannot be learned from data and must be architecturally imposed.

The protocol is independently implementable. This specification defines the structural requirements any conformant implementation must satisfy. The specification is organized into nine parts and an appendix.

Part I: Foundational Concepts

The problem diagnosis, paradigm shift from surveillance to empowerment infrastructure, and the AI-native architecture derivation.

• §1 Problem Architecture — The infrastructure gap, three empirical validations, cybernetic derivation
• §2 Paradigm Shift — Process accountability, cognitive load theory, psychological safety
• §3 AI-Native Architecture — Derivation chain, five properties, trust architecture, standards subsumption

Part II: Core Protocol

The irreducible primitives, behavioral invariants, truth type system, and minimum viable record definitions that constitute the protocol's structural grammar.

• §4 Irreducible Primitives — Nineteen primitives across five tiers, irreducibility tests, composition mechanics
• §5 Behavioral Invariants — Ten invariants in three groups, SHACL enforcement, two-pass validation
• §6 Truth Type System — Four truth types, verification states, claims graduation, promotion path
• §7 Minimum Viable Record — Field definitions for Tier 1 primitives, two-layer SHACL validation

Part III: World Model & Governance Theory

The control-theoretic foundation, symmetry and conservation analysis, and the dimensional business index.

• §8 Control-Theoretic Foundation — Formal derivation chain, VSM structural correspondence, Predictive Composition Pattern
• §9 Symmetry & Conservation — Log-loss breaks organizational symmetries, nine conservation laws
• §10 DBI Perimeter Map — Four-layer observation model, Environment Interface hook

Part IV: Governance & Activation

The governance integration architecture, activation pipeline, and policy projection surfaces.

• §11 Governance Integration — Three-layer governance composition, pattern signatures, ERKIA, AICAR
• §12 Governance Activation — Six-stage activation pipeline, evidence cold start, governance maturity
• §13 Policy Projections — Seven policy domains, projection pipeline, conformance coupling

Part V: Human Interface Layer

Human capture mechanisms, signal and inquiry capture, and decision surface design.

• §14 Human Capture — Unifying capture framework, five design principles, second-order extensions
• §15 Signal Capture — Object-anchored problem capture, seven signal types, flaggable object model
• §16 IQ Capture — Emergence-oriented cognitive overflow, eight intent types, decision-blocking
• §17 Decision Surfaces — Validated Projections, Plan Baseline, Condition Assessment, Prediction

Part VI: Integration & Translation

The integration engine, ontology landscape, and organizational translation machinery.

• §18 Integration Engine — TMI alignment pipeline, three-matcher architecture, graduation engine
• §19 Ontology Landscape — Ten pre-loaded ontology families, primitive alignment, bridge semantics
• §20 TMI Configure — Two-layer architecture, three onboarding paths, progressive depth

Part VII: Profiles & Actors

Substrate profiles, the actor layer, and portfolio patterns for recursive viable systems.

• §21 Substrate Profiles — EAS, BAS, PAS profiles, content packages, graduation pathways
• §22 Actor Layer — Four actor types, RACIVG model, role envelopes, AI-Cannot-Be-Principal
• §23 Portfolio Patterns — Recursive viable systems, tighten-only constraint cascade, federation

Part VIII: Licensing & Ecosystem

The entity licensing structure and license terms architecture.

• §24 Entity & Licensing — Four-layer licensing model, verification architecture, profile graduation
• §25 License Terms — License-as-Constraint architecture, scope binding, federation licensing

Part IX: Implementation

Schema, operations, queries, SDK, and session context layer.

• §26 Implementation Schema — Four schema domains, two-layer SHACL architecture, universal field set
• §27 Operation Catalog — 244+ operations, five-gate authorization, two-pass enforcement
• §28 Query Architecture — 44 query patterns, dual-language (Cypher + SQL), AI-native query stack
• §29 SDK & Workbench — Three SDK layers, MCP server integration, profile-gated workbench
• §30 Session Context Layer — Session lifecycle, checkout model, merge validation

Appendix: AI Orchestration

Agent runtime infrastructure and work orchestration.

• §A1 Agent Runtime — AICAR taxonomy, delegation authority, five-dimensional routing, graduation model
• §A2 Work Orchestration — Resolution engine, constraint mechanics, conformance detection, drift control

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This specification is patent pending. The protocol is open and independently implementable.