# Technical Architecture

**Last Updated:** October 12, 2025
**Audience:** Technical, Implementer, Researcher
**Quadrant:** SYSTEM
**Persistence:** HIGH

---

## Overview

The Tractatus Framework operates as a governance layer that integrates with Claude Code's runtime environment. This document provides a comprehensive technical architecture diagram and explanation of how the components interact.

## System Architecture

The system is organized into four distinct layers that work together to provide robust AI governance:

![Tractatus Architecture Diagram](../../public/images/architecture-diagram.png)

### 1. Claude Code Runtime Environment (Foundation Layer)

**Purpose:** Provides the base LLM environment and session management infrastructure

**Components:**
- **Context Window:** 200,000 token budget for conversation and file content
- **Session Management:** Persistent session state tracking and token checkpoints
- **Tool Access:** Bash, Read, Write, Edit, and other file system operations
- **File System:** Dedicated `.claude/` directory for framework state

**Persistent Files:**
- `.claude/instruction-history.json` - Classified instruction database
- `.claude/session-state.json` - Current session tracking
- `.claude/token-checkpoints.json` - Token milestone records

**Key Features:**
- Session continuity across conversation compactions
- Tool access for framework enforcement
- File system operations for governance rules
- Context window management

---

### 2. Tractatus Governance Layer

**Purpose:** Enforces governance rules and prevents AI failure modes

This layer consists of six core services that monitor, classify, validate, verify, and facilitate pluralistic deliberation:

#### A. BoundaryEnforcer

**Function:** Blocks values decisions requiring human approval

**Blocks:**
- Privacy policy changes
- Ethical trade-offs
- User agency violations
- Strategic mission changes
- Indigenous rights decisions

**Enforcement:** BLOCK_AND_ESCALATE action when boundary violated

**Integration:** Checks all decisions before execution, escalates to human approval

**Example Use Case:** Prevents AI from autonomously deciding privacy policy changes without explicit human approval

---

#### B. InstructionPersistenceClassifier

**Function:** Classifies and stores all explicit instructions

**Classification Dimensions:**
- **Quadrant:** STRATEGIC, OPERATIONAL, TACTICAL, SYSTEM, STOCHASTIC
- **Persistence Level:** HIGH (session-permanent), MEDIUM (contextual), LOW (transient)
- **Temporal Scope:** permanent, session, task, immediate

**Storage:** Appends to `.claude/instruction-history.json` with metadata

**Integration:** Intercepts all user instructions for classification before execution

**Example Use Case:** User says "Always use MongoDB port 27027" → Classified as SYSTEM/HIGH/session → Stored for validation

---

#### C. CrossReferenceValidator

**Function:** Prevents pattern bias from overriding explicit instructions

**Validation:**
- Queries `.claude/instruction-history.json` for conflicting instructions
- Detects contradictions (e.g., specified port vs. default port)
- Blocks actions that violate HIGH persistence instructions

**Enforcement:** BLOCK action when conflict detected

**Integration:** Called before database operations, config changes, architecture decisions

**Example Use Case:** The 27027 Incident - AI attempted to use default port 27017, validator caught conflict with explicit instruction to use 27027

---

#### D. ContextPressureMonitor

**Function:** Detects degraded operating conditions before failure

**Monitoring:**
- **Token Budget:** Tracks usage against 200k limit
- **Message Count:** Monitors conversation length
- **Error Accumulation:** Counts failures and retries
- **Checkpoint Reporting:** Mandatory reporting at 25%, 50%, 75% milestones

**Pressure Levels:**
- NORMAL (0-30%): Standard operations
- ELEVATED (30-50%): Increased vigilance
- HIGH (50-70%): Degraded performance expected
- CRITICAL (70-90%): Major failures likely
- DANGEROUS (90%+): Framework collapse imminent

**Integration:** Reports pressure to user at checkpoints, recommends actions

**Example Use Case:** At 107k tokens (53.5%), monitor detects ELEVATED pressure and warns user of potential pattern bias

---

#### E. MetacognitiveVerifier

**Function:** Self-checks complex operations before execution

**Triggers:**
- Operations affecting >3 files
- Workflows with >5 steps
- Architecture changes
- Security implementations

**Verification:**
- Alignment with user intent
- Coherence of approach
- Completeness of solution
- Safety considerations
- Alternative approaches

**Output:** Confidence score + alternatives

**Integration:** Selective mode - only for complex operations

**Example Use Case:** Before deploying 8-file deployment package, verifies all components align with user requirements and checks for missing pieces

---

#### F. PluralisticDeliberationOrchestrator

**Function:** Facilitates multi-stakeholder deliberation when values conflict without imposing hierarchy

**Triggers:**
- BoundaryEnforcer flags values decision
- Privacy vs. safety trade-offs
- Individual rights vs. collective welfare tensions
- Cultural values conflicts (Western vs. Indigenous, secular vs. religious)
- Policy decisions affecting diverse communities

**Process:**
1. **Values Conflict Detection:** Identifies moral frameworks in tension (deontological, consequentialist, virtue ethics, care ethics, communitarian)
2. **Stakeholder Identification:** Determines affected groups (requires human approval of stakeholder list)
3. **Structured Deliberation:** Facilitates rounds of discussion without imposing value ranking
4. **Outcome Documentation:** Records values prioritized/deprioritized, moral remainder, dissenting views, review date
5. **Precedent Creation:** Stores informative (not binding) precedent with applicability scope

**Enforcement:** AI facilitates deliberation, humans decide (TRA-OPS-0002)

**Integration:**
- Triggered by BoundaryEnforcer when value conflicts detected
- Uses AdaptiveCommunicationOrchestrator for culturally appropriate communication
- Stores precedents in precedent database (informative, not binding)
- Documents moral remainder (what's lost in decisions)

**Example Use Case:** User data disclosure decision - convenes privacy advocates, harm prevention specialists, legal team, affected users. Structured deliberation across frameworks. Decision: Disclose for imminent threat only. Documents privacy violation as moral remainder. Records dissent from privacy advocates. Sets 6-month review.

**Key Principles:**
- Foundational Pluralism: No universal value hierarchy (privacy > safety or safety > privacy)
- Legitimate Disagreement: Valid outcome when values genuinely incommensurable
- Adaptive Communication: Prevents linguistic hierarchy (formal academic, Australian direct, Māori protocol, etc.)
- Provisional Decisions: Reviewable when context changes

---

### 3. MongoDB Persistence Layer

**Purpose:** Stores governance rules, audit logs, and operational state

#### A. governance_rules Collection

**Schema:**
```json
{
  "rule_id": "STR-001",
  "quadrant": "STRATEGIC",
  "persistence": "HIGH",
  "title": "Human Approval for Values Decisions",
  "content": "All decisions involving privacy, ethics...",
  "enforced_by": "BoundaryEnforcer",
  "violation_action": "BLOCK_AND_ESCALATE",
  "examples": ["Privacy policy changes", "Ethical trade-offs"],
  "rationale": "Values decisions cannot be systematized",
  "active": true,
  "created_at": "2025-10-12T00:00:00.000Z",
  "updated_at": "2025-10-12T00:00:00.000Z"
}
```

**Indexes:**
- `rule_id` (unique)
- `quadrant`
- `persistence`
- `enforced_by`
- `active`

**Usage:** Governance services query this collection for enforcement rules

---

#### B. audit_logs Collection

**Schema:**
```json
{
  "timestamp": "2025-10-12T07:30:15.000Z",
  "service": "BoundaryEnforcer",
  "action": "BLOCK",
  "instruction": "Change privacy policy to share user data",
  "rule_violated": "STR-001",
  "session_id": "2025-10-07-001",
  "user_notified": true,
  "human_override": null
}
```

**Indexes:**
- `timestamp`
- `service`
- `session_id`
- `rule_violated`

**Usage:** Comprehensive audit trail for governance enforcement

---

#### C. session_state Collection

**Schema:**
```json
{
  "session_id": "2025-10-07-001",
  "token_count": 62000,
  "message_count": 45,
  "pressure_level": "ELEVATED",
  "pressure_score": 35.2,
  "last_checkpoint": 50000,
  "next_checkpoint": 100000,
  "framework_active": true,
  "services_active": {
    "BoundaryEnforcer": true,
    "InstructionPersistenceClassifier": true,
    "CrossReferenceValidator": true,
    "ContextPressureMonitor": true,
    "MetacognitiveVerifier": true,
    "PluralisticDeliberationOrchestrator": true
  },
  "started_at": "2025-10-12T06:00:00.000Z",
  "updated_at": "2025-10-12T07:30:15.000Z"
}
```

**Usage:** Real-time session monitoring and pressure tracking

---

#### D. instruction_history Collection

**Schema:**
```json
{
  "instruction_id": "inst_001",
  "content": "Always use MongoDB port 27027 for this project",
  "classification": {
    "quadrant": "SYSTEM",
    "persistence": "HIGH",
    "temporal_scope": "session"
  },
  "enforced_by": ["CrossReferenceValidator"],
  "active": true,
  "created_at": "2025-10-12T06:15:00.000Z",
  "expires_at": null,
  "session_id": "2025-10-07-001"
}
```

**Indexes:**
- `instruction_id` (unique)
- `classification.quadrant`
- `classification.persistence`
- `active`
- `session_id`

**Usage:** CrossReferenceValidator queries for conflicts, InstructionPersistenceClassifier writes

---

### 4. API & Web Interface Layer

**Purpose:** Provides programmatic and user access to governance features

#### A. API Endpoints

**Demo Endpoints:**
- `POST /api/demo/classify` - Instruction classification demo
- `POST /api/demo/boundary-check` - Boundary enforcement demo
- `POST /api/demo/pressure-check` - Context pressure calculation demo

**Admin Endpoints:**
- `POST /api/admin/rules` - Manage governance rules
- `GET /api/admin/audit-logs` - View audit trail
- `GET /api/admin/sessions` - Session monitoring

**Auth Endpoints:**
- `POST /api/auth/login` - Admin authentication
- `POST /api/auth/logout` - Session termination

**Health Endpoint:**
- `GET /api/health` - System health check

---

#### B. Web Interface

**Interactive Demos:**
- Classification Demo (`/demos/classification-demo.html`)
- Boundary Enforcement Demo (`/demos/boundary-demo.html`)
- 27027 Incident Visualizer (`/demos/27027-demo.html`)
- Context Pressure Monitor (`/demos/tractatus-demo.html`)

**Admin Dashboard:**
- Rule management interface
- Audit log viewer
- Session monitoring
- Media triage (AI-assisted moderation)

**Documentation:**
- Markdown-based documentation system
- Interactive search with faceted filtering
- PDF exports of key documents
- Architecture diagrams

**Blog System:**
- AI-curated blog post suggestions
- Human approval workflow
- Category-based organization

**Case Submissions:**
- Public submission form
- AI relevance analysis
- Admin moderation queue

**Media Inquiry:**
- Journalist contact form
- AI-assisted triage
- Priority assessment

---

## Data Flow

### 1. User Action → Governance Check → Execution

```
User issues instruction
    ↓
InstructionPersistenceClassifier classifies & stores
    ↓
CrossReferenceValidator checks for conflicts
    ↓
BoundaryEnforcer checks for values decisions
    ↓
    [IF VALUES DECISION DETECTED]
    ↓
PluralisticDeliberationOrchestrator facilitates deliberation
    (Identifies stakeholders → Structures discussion → Documents outcome)
    ↓
Human approval required
    ↓
ContextPressureMonitor assesses current pressure
    ↓
MetacognitiveVerifier checks complexity (if triggered)
    ↓
Action executes OR blocked with explanation
    ↓
Audit log entry created
```

### 2. Session Initialization Flow

```
Claude Code starts session
    ↓
scripts/session-init.js runs
    ↓
Load .claude/instruction-history.json
    ↓
Reset token checkpoints
    ↓
Initialize ContextPressureMonitor
    ↓
Verify all 6 services operational
    ↓
Report framework status to user
```

### 3. Context Pressure Monitoring Flow

```
Every 50k tokens (25% increments)
    ↓
ContextPressureMonitor calculates score
    ↓
Pressure level determined (NORMAL/ELEVATED/HIGH/CRITICAL/DANGEROUS)
    ↓
MANDATORY report to user with format:
    "📊 Context Pressure: [LEVEL] ([SCORE]%) | Tokens: [X]/200000 | Next: [Y]"
    ↓
Recommendations provided if elevated
```

### 4. The 27027 Incident Prevention Flow

```
User explicitly instructs: "Use MongoDB port 27027"
    ↓
InstructionPersistenceClassifier:
    Quadrant: SYSTEM, Persistence: HIGH, Scope: session
    Stores in .claude/instruction-history.json
    ↓
[107k tokens later, context pressure builds]
    ↓
AI attempts to use default port 27017 (pattern recognition)
    ↓
CrossReferenceValidator intercepts:
    Queries instruction_history.json
    Finds conflict: "User specified 27027, AI attempting 27017"
    BLOCKS action
    ↓
User notified: "CONFLICT DETECTED: User specified port 27027..."
    ↓
AI corrects and uses 27027
    ↓
Audit log created:
    service: "CrossReferenceValidator"
    action: "BLOCK"
    rule_violated: "SYS-001"
```

---

## Integration Points

### Claude Code ↔ Tractatus

**1. Tool Access Integration:**
- Tractatus uses Bash tool to run governance scripts
- Read/Write tools access `.claude/` directory for state
- Session state persisted across conversation compactions

**2. Framework Enforcement:**
- Pre-action checks before file operations
- Instruction classification on user input
- Pressure monitoring via token tracking

**3. Session Continuity:**
- `scripts/session-init.js` runs on session start/continuation
- `.claude/session-state.json` maintains active status
- Token checkpoints saved for resumption

---

### Tractatus ↔ MongoDB

**1. Rule Enforcement:**
- Governance services query `governance_rules` for enforcement
- Active rules loaded into memory for performance
- Rules can be dynamically updated via admin interface

**2. Audit Trail:**
- All governance actions logged to `audit_logs`
- Timestamp, service, action, rule_violated recorded
- Queryable for compliance and analysis

**3. Instruction Persistence:**
- InstructionPersistenceClassifier writes to `instruction_history`
- CrossReferenceValidator queries for conflicts
- HIGH persistence instructions remain active across sessions

---

## Deployment Architecture

### Production Environment

**Components:**
- **Docker Compose:** Orchestrates MongoDB + Node.js application
- **MongoDB 7.0:** Database with authentication and persistence
- **Node.js 18:** Application runtime with health checks
- **Systemd:** Process management on Linux servers
- **Nginx:** Reverse proxy with SSL termination (optional)

**Docker Services:**
```yaml
services:
  mongodb:
    image: mongo:7.0
    volumes: [mongodb_data:/data/db]
    healthcheck: [mongosh ping check]

  tractatus-app:
    build: [multi-stage Dockerfile]
    ports: ["9000:9000"]
    depends_on: [mongodb]
    healthcheck: [/api/health check]
    environment: [6 governance service toggles]
```

**Security:**
- Non-root container user (nodejs:1001)
- NoNewPrivileges, PrivateTmp, ProtectSystem
- Content Security Policy enforcement
- CORS protection
- Rate limiting

**See:** [Deployment Quickstart Kit](/downloads/tractatus-quickstart.tar.gz) for complete Docker deployment

---

## Performance Characteristics

### Overhead Measurements

**BoundaryEnforcer:** <5ms per check
**InstructionPersistenceClassifier:** <10ms classification + storage
**CrossReferenceValidator:** <15ms query + validation
**ContextPressureMonitor:** <5ms calculation
**MetacognitiveVerifier:** 50-200ms (complex operations only)
**PluralisticDeliberationOrchestrator:** Variable (depends on deliberation complexity, human-in-the-loop)

**Total Framework Overhead:** <10ms average per operation (excluding human deliberation time)

**Benchmark Results:**
- 223/223 tests passing
- 127 governance-sensitive scenarios validated
- 100% HIGH persistence instruction enforcement
- 0 false negatives in 27027 incident testing

---

## Scalability Considerations

### Horizontal Scaling

**Stateless Services:**
- API endpoints can be load-balanced
- MongoDB replica set for high availability
- Session state in database, not memory

**Bottlenecks:**
- MongoDB query performance (mitigated by indexes)
- Instruction history size (mitigated by archival)

---

### Vertical Scaling

**Memory Requirements:**
- Base application: 200-400 MB
- Per-session overhead: 10-50 MB
- MongoDB: 1-2 GB (moderate rule set)

**Recommended Resources:**
- Development: 2 GB RAM, 2 CPU cores
- Production: 4 GB RAM, 4 CPU cores
- Database: 10 GB disk minimum

---

## Complementarity with Claude Code

**Tractatus does NOT replace Claude Code. It extends it.**

### What Claude Code Provides

✓ Base LLM environment and context window
✓ Tool access (Bash, Read, Write, Edit)
✓ Session management and file operations
✓ Conversation history and compaction
✓ Multi-tool orchestration

### What Tractatus Adds

✓ Instruction persistence and classification
✓ Boundary enforcement for values decisions
✓ Pattern bias detection and prevention
✓ Context pressure monitoring
✓ Complex operation verification
✓ Pluralistic deliberation facilitation (multi-stakeholder, non-hierarchical)
✓ Comprehensive audit trail
✓ Governance rule management

### Integration Benefits

**Together:** Claude Code provides the foundation, Tractatus provides the guardrails

**Example:** Claude Code enables AI to edit files. Tractatus helps ensure AI doesn't violate explicit instructions or cross values boundaries when doing so.

---

## Document Metadata

<div class="document-metadata">

- **Version:** 1.0
- **Created:** 2025-10-12
- **Last Modified:** 2025-10-13
- **Author:** Tractatus Framework Team
- **Word Count:** 2,120 words
- **Reading Time:** ~11 minutes
- **Document ID:** technical-architecture
- **Status:** Active

</div>

---

## License

Copyright 2025 John Stroh

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at:

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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---

## Related Documentation

- [Implementation Guide](/docs/markdown/implementation-guide.md) - How to deploy and configure
- [Core Concepts](/docs/markdown/core-concepts.md) - Governance framework concepts
- [Case Studies](/docs/markdown/case-studies.md) - Real-world failure mode examples
- [Deployment Quickstart](/downloads/tractatus-quickstart.tar.gz) - 30-minute Docker deployment

---

## Technical Support

**Documentation:** https://agenticgovernance.digital/docs
**GitHub:** https://github.com/AgenticGovernance/tractatus-framework
**Email:** research@agenticgovernance.digital
**Interactive Demos:** https://agenticgovernance.digital/demos

---

**Version:** 1.0
**Last Updated:** October 12, 2025
**Maintained By:** Tractatus Framework Team
