CORE: The Last Programmer You Will Ever Need

A Constitutional AI Framework for Autonomous System Building

Document Status: Constitutional (Read-Only) Version: 1.0.0 Date: November 30, 2025 Author: Darek (CORE Architect)

Abstract

CORE (Constitutional Orchestration & Reasoning Engine) is not a code generator, not a linter, not an IDE plugin. CORE is a constitutional AI framework that orchestrates Large Language Models to build complete systems—from simple scripts to operating systems—while ensuring every component is governable, maintainable, and constitutionally compliant.

The fundamental insight: LLMs provide creativity; constitutions provide correctness. By separating what the user wants (intent) from how it's built (execution) and what's allowed (governance), CORE solves the AI reliability problem that plagues every other autonomous coding tool.

This document is the immutable specification of CORE's vision, architecture, and path forward.

The Genesis

The LIRA Project

CORE's origin lies in a failed project called LIRA—a system designed to:

Map organizational process documentation
Cross-reference everything against regulations, standards, and best practices
Produce coverage and maturity maps
Provide actionable compliance insights

LIRA was almost complete when a critical realization emerged: the system designed to ensure governance had no governance itself.

The codebase suffered from:

Massive code duplication
Zero standardization
Inconsistent validation logic
No composable operations
Unmaintainable complexity

The Question: How can a system ensure compliance when it can't ensure its own compliance?

The Answer: Build the governed system first. Then use it to build everything else.

That governed system became CORE.

The Insight Cascade

Three realizations led to CORE's architecture:

Realization 1: Atomic Actions Are Fundamental

"CORE should consist of different atomic actions that get somehow organized to deliver to set goal."

Every operation—whether checking, fixing, generating, or analyzing—is an atomic action with a standard contract. Actions compose into workflows. Workflows achieve goals.

Realization 2: Governance Must Be Constitutional

"Do not code what I say, code what I mean."

Users don't care about governance—they want working solutions. But "working" includes being secure, compliant, maintainable, and correct. Constitutional governance makes this automatic and invisible.

Realization 3: LLMs Need Guardrails

"LLMs are getting better, but did they stop hallucinating?"

LLMs are incredibly creative but fundamentally unreliable. They hallucinate imports, APIs, facts, and code patterns. Constitutional validation catches these hallucinations before they ship.

The Synthesis: CORE orchestrates LLMs to build systems while constitutional governance ensures correctness at every layer.

The Problem Space

The LLM Reliability Crisis

Current AI coding tools (Copilot, Cursor, Devin, etc.) suffer from a fundamental problem:

LLMs hallucinate constantly:

Imports that don't exist
APIs with wrong signatures
Libraries that aren't installed
Design patterns that don't apply
Best practices that don't exist
Regulations that are fabricated

Example:

# LLM generates:
from email_validator import validate_email  # ❌ Not installed
from company_auth import SSOProvider  # ❌ Doesn't exist
from gdpr_compliance import anonymize_pii  # ❌ Hallucinated

def process_user_data(email, data):
    if validate_email(email):  # ❌ Will crash
        auth = SSOProvider.authenticate(email)  # ❌ Will crash
        clean_data = anonymize_pii(data)  # ❌ Will crash
        return clean_data

Current Solution: Human reviews and fixes manually.

Problem: This doesn't scale. Complex systems have thousands of components.

The Governance Gap

Traditional development has governance through:

Code review (humans catch issues)
Testing (automated verification)
CI/CD (enforced checks)
Standards documents (manual compliance)

AI-generated code breaks this:

Too much code for humans to review thoroughly
Tests don't catch architectural violations
CI/CD can't validate design decisions
LLMs don't read standards documents

Result: AI-generated code is fast but ungoverned.

The Intent Translation Problem

Users don't speak in technical specifications:

User says: "Build me a SharePoint data collector"

User means:

Collect specific data fields I care about
Don't violate GDPR
Don't create security vulnerabilities
Use our company's SSO
Handle errors gracefully
Make it maintainable
Follow our coding standards

LLM hears: "Build SharePoint API client"

LLM generates: A technically functional API client that violates half the unstated requirements.

The Gap: No system translates user intent into constrained technical requirements.

The Multi-Domain Challenge

Real projects span multiple domains:

Example: "Build a mobile app with offline sync"

Requires:

Mobile UI (Kotlin/Swift)
Local database (SQLite)
Sync service (Python/Node)
API backend (Python/Go)
Infrastructure (Docker/Kubernetes)
Monitoring (Prometheus/Grafana)

Current AI tools: Generate each piece independently, hope they integrate.

Problem: No constitutional governance across domains. Integration is manual.

The Core Insight

The Fundamental Principle

LLMs provide creativity. Constitutions provide correctness.

This is not a compromise—it's a superposition:

LLMs explore the solution space (creative, fast, broad)
Constitutions constrain the solution space (correct, safe, compliant)

Together: Creative solutions that are guaranteed correct.

The Mind-Body-Will Separation

CORE's architecture separates three concerns:

Mind (Constitutional Layer):

What: Defines truth, correctness, and validity
How: YAML policies, pattern definitions, constraint specifications
Who: Humans (developers, architects, compliance officers)
Examples: "All functions must have IDs", "No hardcoded credentials", "GDPR compliance required"

Body (Execution Layer):

What: Performs actual work
How: Code execution, file operations, API calls
Who: Machines (executors, generators, transformers)
Examples: Parsing files, generating code, running tests

Will (Orchestration Layer):

What: Decides when and how to act
How: Workflow planning, action sequencing, error handling
Who: AI agents (planners, orchestrators, decision-makers)
Examples: "Run checks before fixes", "Retry on transient failure"

Why This Matters:

Traditional AI:

LLM decides WHAT to build, HOW to build it, and WHETHER it's correct

(Unreliable—LLM can't self-validate)

CORE:

Human defines WHAT's correct (Mind)
Machine executes HOW to build (Body)
AI decides WHEN to execute (Will)

(Reliable—separation of concerns, checks and balances)

"Do Not Code What I Say, Code What I Mean"

This phrase captures CORE's value proposition:

User says: "Build a data collector"

Traditional AI codes: A data collector (literal interpretation)

CORE codes:

A data collector (functional requirement)
With GDPR compliance (unstated constraint)
With secure authentication (unstated constraint)
With audit logging (unstated constraint)
With error handling (unstated constraint)
Following company patterns (unstated constraint)

How CORE knows what user means:

Constitutional policies define implicit requirements
Dialogue extracts explicit requirements
Domain knowledge adds technical requirements
Synthesis produces complete specification

The Result: User gets what they MEANT, not what they SAID.

The Vision

The Ultimate Goal

CORE will be the last programmer you will ever need.

This is not hyperbole. Here's what it means:

For Simple Tasks:

User: "Fix the imports in my Python project"
CORE: [Analyzes] [Fixes] [Validates] "Done. 47 files updated."

For Medium Tasks:

User: "Build me a REST API for customer management"
CORE: [Dialogue] "What fields? Authentication method? Database?"
User: [Answers]
CORE: [Generates complete API with tests, docs, deployment]

For Complex Tasks:

User: "Create a mobile OS compatible with Windows apps"
CORE: [Extended dialogue to understand intent]
CORE: [Produces 100-page specification]
User: "Approved"
CORE: [Orchestrates 6-month project across multiple domains]
CORE: [Delivers working OS]

Not a Tool—A System

CORE is not:

❌ A linter that catches errors
❌ A code generator that writes functions
❌ An IDE plugin that autocompletes
❌ A CI/CD system that runs tests

CORE is:

✅ A constitutional AI framework
✅ That orchestrates LLMs
✅ To build complete systems
✅ Across arbitrary domains
✅ With guaranteed governance

The Transformation

Before CORE:

User has idea
  → User writes specification
    → User writes code
      → User debugs
        → User adds tests
          → User reviews compliance
            → User fixes violations
              → Repeat until correct

(Weeks to months. Error-prone. Requires expertise.)

With CORE:

User has idea
  → User talks with CORE
    → CORE understands intent
      → CORE builds system
        → CORE ensures compliance
          → User gets working solution

(Hours to days. Constitutional guarantees. No expertise required.)

Scope of "Everything"

When we say "build anything," we mean it:

Software Domains:

Web applications (React, Vue, Angular)
Mobile apps (Android, iOS)
Backend services (Python, Go, Java)
Desktop applications (Electron, native)
Embedded systems (C, Rust)
Operating systems (C, assembly)
Databases (SQL schemas, migrations)
Infrastructure (Terraform, Kubernetes)

Document Domains:

Technical documentation
API specifications
Architecture documents
Compliance reports
Process documentation

Analysis Domains:

Code analysis (LIRA's original goal)
Architecture review
Security audit
Compliance verification
Performance profiling

Why This Is Possible:

Each domain has patterns
Patterns are constitutional
LLMs generate within patterns
Constitution validates outputs

Same Framework. Different Domains. Universal Governance.

The Architecture

The Layers (Detailed)

┌─────────────────────────────────────────────────────────────┐
│                    USER INTERFACE                           │
│  Natural language, chat, or structured requests             │
└────────────────────┬────────────────────────────────────────┘
                     ↓
┌─────────────────────────────────────────────────────────────┐
│              DIALOGUE AGENT (Intent Extraction)             │
│                                                             │
│  Purpose: Understand what user MEANS, not just says         │
│                                                             │
│  Process:                                                   │
│  1. Parse initial request                                   │
│  2. Identify ambiguities and gaps                           │
│  3. Ask clarifying questions                                │
│  4. Build mental model of intent                            │
│  5. Generate NorthStar Document                             │
│                                                             │
│  Output: NorthStar (user's true intent)                     │
└────────────────────┬────────────────────────────────────────┘
                     ↓
┌─────────────────────────────────────────────────────────────┐
│         REQUIREMENTS AGENT (Constraint Addition)            │
│                                                             │
│  Purpose: Translate intent into executable requirements     │
│                                                             │
│  Process:                                                   │
│  1. Analyze NorthStar                                       │
│  2. Identify functional requirements                        │
│  3. Add constitutional constraints (from Mind)              │
│  4. Add domain-specific requirements                        │
│  5. Define success criteria                                 │
│  6. Generate High-Level Requirements Document               │
│                                                             │
│  Output: Requirements (functional + constitutional)         │
└────────────────────┬────────────────────────────────────────┘
                     ↓
┌─────────────────────────────────────────────────────────────┐
│           ARCHITECT AGENT (System Design)                   │
│                                                             │
│  Purpose: Decompose requirements into buildable components  │
│                                                             │
│  Process:                                                   │
│  1. Analyze requirements                                    │
│  2. Identify necessary components                           │
│  3. Define component interfaces                             │
│  4. Specify dependencies                                    │
│  5. Create project structure                                │
│  6. Generate Architecture Document                          │
│                                                             │
│  Output: Architecture (components + interfaces)             │
└────────────────────┬────────────────────────────────────────┘
                     ↓
┌─────────────────────────────────────────────────────────────┐
│            PLANNER AGENT (Execution Strategy)               │
│                                                             │
│  Purpose: Create executable workflow from architecture      │
│                                                             │
│  Process:                                                   │
│  1. Analyze architecture                                    │
│  2. Identify atomic actions needed                          │
│  3. Determine dependencies and ordering                     │
│  4. Create workflow phases                                  │
│  5. Assign actions to phases                                │
│  6. Define abort and retry policies                         │
│  7. Generate Execution Plan                                 │
│                                                             │
│  Output: Workflow (DAG of atomic actions)                   │
└────────────────────┬────────────────────────────────────────┘
                     ↓
┌─────────────────────────────────────────────────────────────┐
│          EXECUTOR AGENTS (Implementation)                   │
│                                                             │
│  Purpose: Execute atomic actions with LLM assistance        │
│                                                             │
│  For each atomic action:                                    │
│  ┌──────────────────────────────────────────────────────┐   │
│  │ 1. LLM Generation Phase                              │   │
│  │    - LLM generates code/config/docs                  │   │
│  │    - Creative, broad solution space                  │   │
│  │                                                      │   │
│  │ 2. Constitutional Validation Phase                   │   │
│  │    - Verify against policies                         │   │
│  │    - Check for hallucinations                        │   │
│  │    - Validate patterns                               │   │
│  │                                                      │   │
│  │ 3. Remediation Phase (if needed)                     │   │
│  │    - Identify specific violations                    │   │
│  │    - Retry with constraints                          │   │
│  │    - Or apply constitutional templates               │   │
│  │                                                      │   │
│  │ 4. Integration Phase                                 │   │
│  │    - Verify component interfaces                     │   │
│  │    - Check cross-component consistency               │   │
│  │    - Run integration tests                           │   │
│  └──────────────────────────────────────────────────────┘   │
│                                                             │
│  Output: ActionResult (success/failure + artifacts)         │
└────────────────────┬────────────────────────────────────────┘
                     ↓
┌─────────────────────────────────────────────────────────────┐
│         CONSTITUTIONAL LAYER (The Guardian)                 │
│                                                             │
│  Runs at EVERY step:                                        │
│                                                             │
│  Pre-Execution:                                             │
│    - Validate action has required metadata                  │
│    - Check policies exist and are current                   │
│    - Verify action authorized for domain                    │
│                                                             │
│  During Execution:                                          │
│    - Monitor for policy violations                          │
│    - Log all state changes                                  │
│    - Enforce resource limits                                │
│                                                             │
│  Post-Execution:                                            │
│    - Validate result structure                              │
│    - Verify postconditions met                              │
│    - Check for hallucinations                               │
│    - Store for governance review                            │
│                                                             │
│  Output: Validation results, violations (if any)            │
└─────────────────────────────────────────────────────────────┘

The Data Flow

1. User Input → NorthStar

User: "Build a customer management system with mobile access"

Dialogue Agent asks:
- "What customer data do you need to track?"
- "Should it work offline?"
- "What's your authentication method?"
- "Any regulatory requirements (GDPR, etc.)?"
- "Performance expectations?"

NorthStar Document:
{
  intent: "Customer relationship management with mobile-first design",
  domain: ["web_application", "mobile_application", "database"],
  constraints: ["gdpr_compliant", "offline_capable", "sso_auth"],
  priorities: {
    user_experience: 10,
    security: 10,
    performance: 8,
    cost: 6
  },
  success_criteria: [
    "Works offline on mobile",
    "Syncs when online",
    "GDPR-compliant data handling",
    "SSO integration",
    "Response time <500ms"
  ]
}

2. NorthStar → Requirements

Requirements Document:

Functional:
- Web dashboard for customer management
- Mobile app (iOS + Android) with offline mode
- REST API for data access
- PostgreSQL database with encryption
- Real-time sync service

Constitutional:
- GDPR: PII encryption, right to deletion, audit logging
- Security: SSO authentication, role-based access, TLS everywhere
- Performance: Database indexing, caching layer, CDN for assets
- Maintainability: Docker containers, automated tests, monitoring

Technical:
- Frontend: React (web), React Native (mobile)
- Backend: Python FastAPI
- Database: PostgreSQL with encryption extension
- Infrastructure: Docker Compose (dev), Kubernetes (prod)
- Monitoring: Prometheus + Grafana

3. Requirements → Architecture

Architecture Document:

Components:
1. Web UI (React)
   - Customer list/detail views
   - Search and filtering
   - CRUD operations

2. Mobile App (React Native)
   - Offline-first architecture
   - Local SQLite cache
   - Background sync service

3. API Gateway (FastAPI)
   - REST endpoints
   - SSO integration
   - Rate limiting
   - Request validation

4. Sync Service (Python)
   - Conflict resolution
   - Event-driven updates
   - Webhook support

5. Database (PostgreSQL)
   - Encrypted tables
   - Audit logging
   - Backup automation

Interfaces:
- Web → API: REST/JSON
- Mobile → API: REST/JSON
- API → DB: SQL/ORM
- Sync → API: WebSocket

Constitutional Mappings:
- GDPR → Encryption module, Audit service, Deletion workflow
- Security → Auth middleware, TLS config, Secret management
- Performance → Caching layer, DB indexes, Query optimization

4. Architecture → Workflow

Execution Plan:

Phase 1: Foundation
Actions:
- create.project_structure
- setup.docker_environment
- initialize.git_repository

Phase 2: Database
Actions:
- generate.database_schema (with encryption)
- create.migration_scripts
- setup.backup_automation

Phase 3: API Layer
Actions:
- generate.api_endpoints
- implement.authentication (SSO)
- add.request_validation
- create.api_tests

Phase 4: Web UI
Actions:
- generate.react_components
- implement.customer_views
- add.state_management
- create.ui_tests

Phase 5: Mobile App
Actions:
- generate.react_native_app
- implement.offline_storage
- add.sync_service
- create.mobile_tests

Phase 6: Integration
Actions:
- test.end_to_end_flows
- verify.constitutional_compliance
- setup.monitoring
- create.deployment_configs

Each action: LLM generates → Constitution validates → Result recorded

5. Workflow → Execution → Artifacts

For each action, example: generate.api_endpoints

LLM Phase:
Input: "Create FastAPI endpoints for customer CRUD with SSO auth"
Output:
```python
# Generated by LLM
from fastapi import FastAPI, Depends
from auth import verify_sso_token

app = FastAPI()

@app.get("/customers")
async def list_customers(user = Depends(verify_sso_token)):
    # Implementation
    pass

Constitutional Validation:

✅ Check: SSO dependency exists
✅ Check: Endpoint follows naming convention
✅ Check: Has authentication
✅ Check: Has input validation
❌ Check: Missing rate limiting
❌ Check: Missing audit logging

Remediation: LLM regenerates with constraints: "Add rate limiting and audit logging"

Final Output:

# Generated by LLM, validated by Constitution
from fastapi import FastAPI, Depends
from fastapi_limiter import RateLimiter
from auth import verify_sso_token
from audit import log_access

app = FastAPI()

@app.get("/customers")
@RateLimiter(max_requests=100, window_seconds=60)
async def list_customers(user = Depends(verify_sso_token)):
    log_access("list_customers", user.id)
    # Implementation
    pass

ActionResult: { action_id: "generate.api_endpoints", ok: true, data: { files_created: ["api/customers.py", "api/auth.py"], endpoints: 5, constitutional_checks_passed: 12 }, warnings: ["Rate limiter required retry"], duration_sec: 4.2 }

Key Architectural Principles

1. Separation of Concerns

Mind defines truth (humans)
Body executes (machines)
Will orchestrates (AI agents)

2. LLM-Constitution Duality

LLMs generate (creative)
Constitution validates (correct)
Never one without the other

3. Atomic Actions as Building Blocks

Every operation is an atomic action
Actions have standard contracts
Actions compose into workflows
Workflows achieve goals

4. Constitutional Governance Everywhere

No action executes without validation
No output ships without compliance
No hallucination survives to user

5. Domain Agnostic Framework

Same architecture for all domains
Domain-specific policies plug in
Universal governance applies everywhere

The Execution Model

Atomic Actions (The Foundation)

Every operation in CORE is an atomic action with this contract:

@dataclass
class ActionResult:
    """Universal result contract"""

    action_id: str
    """Unique identifier (e.g., 'generate.api', 'check.security')"""

    ok: bool
    """Binary success indicator"""

    data: dict[str, Any]
    """Action-specific structured results"""

    duration_sec: float
    """Execution time"""

    impact: ActionImpact
    """read-only | write-metadata | write-code | write-data"""

    logs: list[str]
    """Debug traces (internal)"""

    warnings: list[str]
    """Non-fatal issues"""

    suggestions: list[str]
    """Recommended follow-up actions"""

Every action—whether checking code, generating documentation, or building an OS—returns this structure.

Action Metadata (Constitutional Definition)

@atomic_action(
    action_id="generate.rest_api",
    intent="Create RESTful API service",
    impact=ActionImpact.WRITE_CODE,
    policies=["api_security", "input_validation", "error_handling"],
    domains=["web_backend"],
    languages=["python"],
)
async def generate_rest_api(spec: APISpec) -> ActionResult:
    """Generate a constitutionally-compliant REST API"""

    # LLM generation
    code = await llm.generate_api(spec)

    # Constitutional validation
    validation = await constitution.validate(
        code=code,
        policies=["api_security", "input_validation", "error_handling"],
        domain="web_backend"
    )

    # Remediation if needed
    if not validation.ok:
        code = await llm.regenerate(
            spec=spec,
            constraints=validation.violations
        )

    return ActionResult(
        action_id="generate.rest_api",
        ok=True,
        data={"files": code.files, "endpoints": code.endpoint_count},
        impact=ActionImpact.WRITE_CODE
    )

Workflow Composition

Actions compose into workflows:

@dataclass
class WorkflowDefinition:
    """Constitutional definition of a workflow"""

    workflow_id: str
    goal: str
    phases: list[WorkflowPhase]
    abort_policy: AbortPolicy

@dataclass
class WorkflowPhase:
    """Logical grouping of actions"""

    name: str
    actions: list[str]  # Action IDs
    critical: bool

Example:

build_api_workflow = WorkflowDefinition(
    workflow_id="build.rest_api",
    goal="Create production-ready REST API",
    phases=[
        WorkflowPhase(
            name="Foundation",
            actions=["create.project_structure", "setup.environment"],
            critical=True
        ),
        WorkflowPhase(
            name="Implementation",
            actions=["generate.models", "generate.endpoints", "generate.tests"],
            critical=True
        ),
        WorkflowPhase(
            name="Deployment",
            actions=["create.dockerfile", "create.k8s_configs"],
            critical=True
        ),
    ],
    abort_policy=AbortPolicy.STOP_ON_CRITICAL_FAILURE
)

The LLM-Constitution Loop

This is where the magic happens:

async def execute_action_with_llm(action: Action) -> ActionResult:
    """Execute action using LLM with constitutional validation"""

    max_retries = 3
    attempt = 0
    constraints = []

    while attempt < max_retries:
        # GENERATION PHASE
        llm_output = await llm.generate(
            prompt=action.prompt,
            constraints=constraints,
            domain=action.domain
        )

        # VALIDATION PHASE
        validation = await constitution.validate(
            output=llm_output,
            policies=action.policies,
            domain=action.domain
        )

        # CHECK FOR SUCCESS
        if validation.ok:
            return ActionResult(
                action_id=action.id,
                ok=True,
                data=llm_output.data,
                warnings=validation.warnings
            )

        # HALLUCINATION DETECTION
        hallucinations = detect_hallucinations(
            output=llm_output,
            validation=validation
        )

        if hallucinations:
            log_hallucinations(action.id, hallucinations)
            constraints.extend([
                f"Do not use: {h}" for h in hallucinations
            ])

        # ADD CONSTRAINTS FOR NEXT ATTEMPT
        constraints.extend(validation.violations)
        attempt += 1

    # FAILED AFTER RETRIES
    return ActionResult(
        action_id=action.id,
        ok=False,
        data={"error": "Max retries exceeded"},
        warnings=["Constitutional compliance not achieved"]
    )

Key Points:

LLM generates freely (creative)
Constitution validates strictly (correct)
Hallucinations detected and excluded
Violations become constraints for retry
Maximum retries prevent infinite loops
Failures are constitutional states, not exceptions

Hallucination Detection

class HallucinationDetector:
    """Detect LLM fabrications using constitutional knowledge"""

    async def detect(self, output: LLMOutput, domain: str) -> list[Hallucination]:
        """Find all hallucinations in LLM output"""

        hallucinations = []

        # Check imports/dependencies
        for imp in output.imports:
            if not self.library_exists(imp, domain):
                hallucinations.append(
                    Hallucination(
                        type="import",
                        value=imp,
                        reason="Library not in dependencies"
                    )
                )

        # Check API signatures
        for call in output.api_calls:
            if not self.signature_matches(call):
                hallucinations.append(
                    Hallucination(
                        type="api",
                        value=call,
                        reason="Function signature mismatch"
                    )
                )

        # Check citations/references
        for ref in output.references:
            if not self.reference_exists(ref):
                hallucinations.append(
                    Hallucination(
                        type="reference",
                        value=ref,
                        reason="Referenced document/regulation doesn't exist"
                    )
                )

        # Check cross-references in code
        for xref in output.cross_references:
            if not self.code_element_exists(xref):
                hallucinations.append(
                    Hallucination(
                        type="cross_reference",
                        value=xref,
                        reason="Referenced code element not found"
                    )
                )

        return hallucinations

The Constitutional Framework

What is a Constitution?

In CORE, a constitution is the complete set of policies, patterns, constraints, and validation rules that define correctness for a domain.

Structure:

.intent/
├── charter/
│   └── patterns/           # Universal patterns
│       ├── atomic_actions.yaml
│       ├── workflows.yaml
│       └── governance.yaml
│
├── domains/                # Domain-specific constitutions
│   ├── python/
│   │   ├── imports.yaml
│   │   ├── naming.yaml
│   │   └── structure.yaml
│   │
│   ├── kernel_c/
│   │   ├── memory_safety.yaml
│   │   ├── concurrency.yaml
│   │   └── error_handling.yaml
│   │
│   └── android_kotlin/
│       ├── lifecycle.yaml
│       ├── security.yaml
│       └── performance.yaml
│
└── policies/               # Cross-domain policies
    ├── gdpr.yaml
    ├── security.yaml
    └── accessibility.yaml

Example Constitution: Python Domain

# .intent/domains/python/imports.yaml

domain: python
subdomain: imports
version: "1.0.0"

policy_id: import_organization
description: |
  Import statements must be grouped and ordered according to PEP 8.
  This ensures consistency and readability across the codebase.

rules:
  - rule_id: import_grouping
    description: "Imports must be in three groups: stdlib, third-party, local"
    severity: error
    validation: |
      1. Standard library imports come first
      2. Blank line
      3. Third-party imports
      4. Blank line
      5. Local application imports

    examples:
      good: |
        import os
        import sys

        import requests
        import pandas

        from myapp import config

      bad: |
        from myapp import config
        import requests
        import os

  - rule_id: alphabetical_ordering
    description: "Within each group, imports must be alphabetically sorted"
    severity: warning

  - rule_id: no_wildcard_imports
    description: "Wildcard imports (from x import *) are prohibited"
    severity: error

validated_by:
  - check.imports

enforced_by:
  - fix.imports

remediation:
  automatic: true
  action: fix.imports
  command: "core-admin fix imports --write"

# .intent/policies/gdpr.yaml

policy_id: gdpr_compliance
version: "1.0.0"
regulation: "EU General Data Protection Regulation"
scope: cross_domain

description: |
  All systems handling personal data must comply with GDPR requirements.
  This policy applies across all domains and languages.

requirements:
  - requirement_id: pii_encryption
    description: "Personal Identifiable Information must be encrypted at rest"
    applies_to: ["database", "file_storage", "backups"]
    validation: |
      - Database columns with PII must use encryption
      - Files with PII must be encrypted
      - Backups must be encrypted

  - requirement_id: audit_logging
    description: "All PII access must be logged"
    applies_to: ["api", "database", "file_access"]
    validation: |
      - Log who accessed PII
      - Log when accessed
      - Log what was accessed
      - Retain logs for required period

  - requirement_id: right_to_deletion
    description: "Users can request complete data deletion"
    applies_to: ["api", "database"]
    validation: |
      - Deletion endpoint exists
      - Cascades to all related data
      - Includes backups
      - Completes within 30 days

  - requirement_id: data_minimization
    description: "Collect only necessary PII"
    applies_to: ["api", "forms", "database"]
    validation: |
      - Each PII field has documented justification
      - No PII collected without explicit consent
      - PII retention period defined

enforcement:
  - On code generation:
    - Database schemas: Auto-add encryption
    - API endpoints: Auto-add audit logging
    - User models: Auto-add deletion cascade

  - On validation:
    - Scan for unencrypted PII storage
    - Verify audit logging present
    - Check deletion endpoints exist

remediation:
  - Add encryption to schemas
  - Add audit middleware
  - Generate deletion endpoints

Domain Registry

@dataclass
class DomainDefinition:
    """Constitutional definition of a domain"""

    domain_id: str
    description: str
    languages: list[str]
    policies: list[str]  # Policy IDs that apply
    patterns: list[str]  # Pattern IDs that apply
    validators: list[str]  # Tools for validation
    generators: list[str]  # Tools for generation

# Example domains
DOMAINS = {
    "python": DomainDefinition(
        domain_id="python",
        description="Python application development",
        languages=["python"],
        policies=["import_organization", "naming_conventions", "docstrings"],
        patterns=["atomic_actions", "dependency_injection"],
        validators=["ruff", "mypy", "pylint"],
        generators=["ast_generator", "template_engine"]
    ),

    "kernel_c": DomainDefinition(
        domain_id="kernel_c",
        description="Linux kernel module development",
        languages=["c"],
        policies=["memory_safety", "concurrency", "error_handling"],
        patterns=["kernel_patterns", "driver_patterns"],
        validators=["sparse", "coccinelle", "smatch"],
        generators=["c_generator", "kernel_template_engine"]
    ),

    "web_frontend": DomainDefinition(
        domain_id="web_frontend",
        description="Web frontend development",
        languages=["javascript", "typescript", "html", "css"],
        policies=["accessibility", "security", "performance"],
        patterns=["react_patterns", "component_architecture"],
        validators=["eslint", "axe", "lighthouse"],
        generators=["react_generator", "component_generator"]
    ),
}

Multi-Domain Projects

When a project spans multiple domains, constitutions compose:

@dataclass
class ProjectConstitution:
    """Constitution for multi-domain project"""

    project_id: str
    domains: list[str]
    cross_domain_policies: list[str]

# Example: Mobile app with native library
mobile_app_constitution = ProjectConstitution(
    project_id="customer_app",
    domains=["android_kotlin", "ios_swift", "native_c"],
    cross_domain_policies=[
        "gdpr_compliance",  # Applies to all domains
        "security",          # Applies to all domains
        "accessibility",     # Applies to UI domains
    ]
)

# When generating code:
for domain in mobile_app_constitution.domains:
    domain_constitution = load_domain_constitution(domain)
    cross_domain = load_policies(mobile_app_constitution.cross_domain_policies)

    full_constitution = merge(domain_constitution, cross_domain)

    # LLM generates with full constitutional context
    code = generate_with_constitution(domain, full_constitution)

The Roadmap

Current State (A2: Constitutional Compliance)

What Works Today:

✅ Constitutional framework (.intent policies)
✅ Atomic actions (fix.ids, fix.headers with ActionResult)
✅ Workflow orchestration (dev.sync with DevSyncReporter)
✅ Activity logging
✅ Python domain support
✅ LLM integration (DeepSeek, Claude)

Status: CORE can enforce constitutional compliance on Python code.

Phase 1: Foundation

Goal: Complete ActionResult migration, prove multi-domain works

Tasks:

Migrate all Python actions to ActionResult
Add C domain support (prove multi-language)
Add Kotlin domain support (prove mobile)
Build simple multi-domain project

Deliverable: CORE generates Python + C + Kotlin with constitutional compliance

Success Criteria:

Zero CommandResult/AuditCheckResult instances
3 domains fully supported
Multi-domain project compiles and runs
All constitutional validations pass

Phase 2: Dialogue System

Goal: Extract user intent through conversation

Tasks:

Build NorthStar generator
Build requirements translator
Build clarification dialogue system
Test with real users

Deliverable: CORE understands what users MEAN

Success Criteria:

User gives vague request
CORE asks clarifying questions
CORE produces accurate NorthStar
User confirms "yes, that's what I meant"

Phase 3: Simple Autonomy

Goal: End-to-end autonomous project building

Tasks:

Build architect agent
Build planner agent
Build multi-domain executor
Integrate dialogue → architecture → execution

Deliverable: Full autonomous workflow

Test Case:

User: "Build me a REST API for customer management"
CORE: [Dialogue] [Architecture] [Execution] [Delivery]
Output: Working API with tests, docs, deployment

Success Criteria:

No user intervention after NorthStar approval
Generated code compiles
All tests pass
Constitutional validation passes
Deployment succeeds

Phase 4: LIRA Rebuilt

Goal: Build LIRA using CORE

Why This Matters:

LIRA was the original motivation
Proves CORE can build complex analysis systems
Validates document domain support
Real-world use case

Tasks:

Add document analysis domain
Add regulatory compliance policies
Build LIRA as CORE workflow
Deploy to real organizations

Deliverable: LIRA 2.0 (built by CORE)

Success Criteria:

Analyzes process documentation
Cross-references regulations
Produces maturity maps
Zero hallucinated citations
Constitutional compliance verified

Phase 5: Complex Multi-Domain Multi-Language(Year 2, Q1-Q2)

Goal: Build complex systems spanning many domains

Test Cases:

E-commerce Platform:

Web frontend (React)
Mobile apps (iOS + Android)
Backend API (Python)
Database (PostgreSQL)
Infrastructure (Kubernetes)
Payment processing

IoT System:

Embedded firmware (C)
Mobile app (React Native)
Cloud backend (Python)
Real-time processing (Rust)
Dashboard (React)

Success Criteria:

All components generate correctly
Integration works first time
Constitutional compliance across all domains
Zero manual fixes needed

Phase 6: Mobile OS (Year 2, Q3-Q4)

Goal: The ultimate test—build an operating system

Project: Android fork with Windows app compatibility

Scope:

AOSP fork (Java, C, C++)
Windows API translation layer (C++)
Security hardening
Performance optimization
Build system
Testing framework
Documentation

Why This Matters:

Proves CORE can handle massive scope
Validates multi-language orchestration
Tests constitutional governance at scale
Ultimate proof of "last programmer"

Success Criteria:

OS boots on real hardware
Windows apps run
Constitutional compliance verified
No critical security vulnerabilities
Performance within targets

Phase 7: Self-Improvement (Year 3+)

Goal: CORE improves CORE (A4)

Capabilities:

Performance Optimization:
CORE profiles its own actions
CORE optimizes slow actions
CORE improves its own code
Pattern Learning:
CORE learns which workflows work best
CORE optimizes action sequences
CORE improves planning
Constitutional Amendment:
CORE proposes policy improvements
Human reviews and approves
CORE updates its own constitution
Self-Replication:
CORE writes CORE.NG (next generation)
Based on functionality, not code
True self-awareness

The Vision: CORE becomes the last version of CORE.

Why This Will Work

1. Proven Foundation

CORE already works for Python:

Constitutional policies validate code
Atomic actions compose into workflows
DevSyncReporter provides beautiful output
Activity logging creates audit trails

Extrapolation: Same architecture applies to other domains.

2. The Right Abstraction

Atomic Actions are universal:

Every operation returns ActionResult
Every action has constitutional metadata
Every action integrates with LLMs
Every action validates with constitution

This scales: Python → C → Any language

3. Separation of Concerns

Mind-Body-Will is robust:

Mind (policies) are domain-specific but structurally identical
Body (execution) is language-specific but pattern-based
Will (orchestration) is universal across all domains

No confusion: Each layer has clear responsibilities.

4. LLM Integration Strategy

Not relying on perfect LLMs:

Assume LLMs will hallucinate
Build detection mechanisms
Add remediation loops
Make constitution the source of truth

Result: Even imperfect LLMs produce perfect output.

5. Incremental Validation

Not building everything at once:

Phase 1: Prove multi-language (Python + C + Kotlin)
Phase 2: Prove intent extraction (dialogue)
Phase 3: Prove simple autonomy (REST API)
Phase 4: Prove complex autonomy (LIRA)
Phase 5: Prove massive autonomy (Mobile OS)

Each phase validates the next: No big-bang risk.

6. Real Use Cases

Not theoretical:

LIRA: Real organizational need
DevOps automation: Real developer pain
Mobile OS: Ambitious but concrete

Market validation: Each phase has customers.

7. Constitutional Governance

The unique advantage:

Others try to make LLMs perfect (impossible)
CORE makes LLMs bounded (achievable)

Result: Reliable AI through governance, not through perfection.

Comparison to Existing Solutions

GitHub Copilot

What it does:

Inline code suggestions
Function/file generation
Chat-based assistance

Strengths:

Fast
Good UX
IDE-integrated

Weaknesses:

No governance
Frequent hallucinations
No multi-file orchestration
No constitutional compliance
No domain policies

CORE's Advantage:

Constitutional validation catches Copilot's hallucinations
Multi-domain orchestration for complete systems
Policies ensure compliance automatically

Cursor/Windsurf

What it does:

Multi-file editing
Codebase understanding
Chat interface

Strengths:

Context-aware
Good for refactoring
Better than Copilot for large changes

Weaknesses:

Still no governance
Still hallucinates
No constitutional framework
Reactive (helps with existing code) not generative (builds from scratch)

CORE's Advantage:

Proactive system building
Constitutional compliance from start
Cross-domain support

Devin (Cognition AI)

What it does:

Autonomous software engineer
Long-running tasks
Tool use (terminal, browser)

Strengths:

Truly autonomous
Can work for hours
Impressive capabilities

Weaknesses:

Can "go off the rails"
No constitutional governance
Expensive failures
Black box decision-making

CORE's Advantage:

Constitutional constraints prevent "going off the rails"
Transparent decision-making
Governable autonomy
Failures are constitutional events, not catastrophes

AutoGPT/BabyAGI

What they do:

Goal-based autonomous agents
Break down tasks
Execute plans

Strengths:

Interesting architecture
Novel approach

Weaknesses:

Unreliable
Hallucinate goals and plans
No validation framework
More research than production

CORE's Advantage:

Production-ready
Constitutional validation at every step
Proven foundation (already working for Python)
Real use cases, not demos

Traditional Development Tools

What they do:

Linters (Ruff, ESLint)
Formatters (Black, Prettier)
Static analysis (MyPy, TypeScript)
CI/CD pipelines

Strengths:

Reliable
Well-established
Integration ecosystem

Weaknesses:

Reactive (catch problems after writing)
No generative capability
Siloed by language
No AI assistance

CORE's Advantage:

Proactive (prevents problems before writing)
Generative (writes code for you)
Universal (all languages)
AI-powered (creative solutions)
But keeps traditional tools' reliability through constitution

The Competitive Matrix

Feature	Copilot	Cursor	Devin	CORE
Code Generation	✅	✅	✅	✅
Multi-File	❌	✅	✅	✅
Autonomous	❌	❌	✅	✅
Constitutional Governance	❌	❌	❌	✅
Multi-Domain	❌	❌	❌	✅
Hallucination Detection	❌	❌	❌	✅
Intent Extraction	❌	❌	⚠️	✅
Complete Systems	❌	❌	⚠️	✅
Governance Audit Trail	❌	❌	❌	✅
Domain Policies	❌	❌	❌	✅

CORE is the only system with constitutional governance for autonomous, multi-domain system building.

Technical Specifications

System Requirements

Core Components:

- Python 3.12+
- PostgreSQL 14+ (for knowledge storage)
- Qdrant (for vector embeddings)
- Docker (for containerization)
- 16GB RAM minimum (32GB recommended)
- SSD storage (for fast vector search)

LLM Integration:

- DeepSeek (code generation, analysis)
- Claude Sonnet 4 (dialogue, planning)
- Local embeddings (sentence transformers)
- Fallback to OpenAI if needed

Supported Languages (Current):

- Python 3.8+

Supported Languages (Roadmap):

- C (kernel, embedded)
- C++ (systems, performance)
- Kotlin (Android)
- Swift (iOS)
- JavaScript/TypeScript (web)
- Rust (systems, safety)
- Go (services, cloud)
- Java (enterprise)

Performance Targets

Action Execution:

Simple actions: <2 seconds
Complex actions: <30 seconds
LLM generation: <10 seconds
Constitutional validation: <1 second

Workflow Execution:

Small workflows (5 actions): <1 minute
Medium workflows (20 actions): <5 minutes
Large workflows (100 actions): <30 minutes

Scalability:

Handle 1000+ files per project
Support 100+ concurrent actions
Store 1M+ action results
Index 10M+ code symbols

API Structure

Core Actions API:

# Execute single action
result = await core.execute_action(
    action_id="generate.api",
    params={"spec": api_spec},
    dry_run=False
)

# Execute workflow
workflow_result = await core.execute_workflow(
    workflow_id="build.rest_api",
    params={"requirements": requirements},
    callbacks={"on_phase_complete": notify_user}
)

# Query constitution
policies = core.constitution.get_policies(domain="python")
validation = await core.constitution.validate(
    code=code,
    policies=["import_organization", "naming"]
)

Dialogue API:

# Start dialogue
session = await core.dialogue.start(
    initial_request="Build customer management system"
)

# Iterate on clarifications
while not session.northstar_ready:
    question = await session.next_question()
    answer = await get_user_input(question)
    await session.provide_answer(answer)

# Get NorthStar
northstar = session.get_northstar()

Planning API:

# Create execution plan
plan = await core.planner.create_plan(
    northstar=northstar,
    constraints=["budget", "timeline"]
)

# Review plan
for phase in plan.phases:
    print(f"Phase: {phase.name}")
    for action in phase.actions:
        print(f"  - {action.id}: {action.intent}")

# Execute plan
result = await core.executor.execute(plan)

Storage Schema

Action Results:

CREATE TABLE action_results (
    id UUID PRIMARY KEY,
    action_id VARCHAR NOT NULL,
    workflow_id VARCHAR,
    ok BOOLEAN NOT NULL,
    data JSONB NOT NULL,
    duration_sec FLOAT,
    created_at TIMESTAMP DEFAULT NOW(),
    INDEX idx_action_id (action_id),
    INDEX idx_workflow_id (workflow_id)
);

Constitutional Policies:

CREATE TABLE policies (
    id UUID PRIMARY KEY,
    policy_id VARCHAR UNIQUE NOT NULL,
    domain VARCHAR,
    version VARCHAR,
    rules JSONB NOT NULL,
    created_at TIMESTAMP DEFAULT NOW()
);

Workflow Runs:

CREATE TABLE workflow_runs (
    id UUID PRIMARY KEY,
    workflow_id VARCHAR NOT NULL,
    status VARCHAR NOT NULL,
    phases JSONB NOT NULL,
    created_at TIMESTAMP DEFAULT NOW(),
    completed_at TIMESTAMP
);

Security Model

Secrets Management:

- Fernet encryption for API keys
- Environment variable injection
- No secrets in code or logs
- Rotation support

Execution Sandboxing:

- Docker containers for action execution
- Resource limits (CPU, memory, time)
- Network restrictions
- Filesystem isolation

Constitutional Enforcement:

- No action bypasses validation
- All state changes logged
- Audit trail immutable
- Policy violations block execution

The Path Forward

Immediate Next Steps

Finish ActionResult Migration (Week 1-4):

Follow MIGRATION_GUIDE.md
Convert all Python commands
Unify reporters
Validate with tests

Document Current State:

Update README with vision
Create architecture diagrams
Write contributor guide

First External Validation

Add Second Language (C):

Create C domain constitution
Build simple C action (generate.c_function)
Validate with kernel coding standards
Prove multi-language works

Blog Post / Demo:

"Constitutional AI for Reliable Code Generation"
Show Python + C working together
Demonstrate hallucination detection
Get community feedback

First Real Product

Build Something Useful:

Simple web service generator
Or CLI tool generator
Or documentation generator
Something people can actually use

Open Source Release:

Clean up codebase
Write comprehensive docs
Create contribution guidelines
Announce on relevant forums

Gather Community

Engage AI Safety Researchers:

Constitutional AI is their language
Scalable oversight is their problem
CORE is a practical solution
Seek feedback and collaboration

Engage Developers:

Show how CORE saves time
Demonstrate governance benefits
Prove reliability improvements
Build user base

Scale Up

Add More Domains:
Web (JS/TS)
Mobile (Kotlin/Swift)
Data (SQL)
Infrastructure (YAML/HCL)
Build Complex Projects:
Full-stack applications
Multi-service systems
Complete deployments
Prove Value:
Time saved metrics
Quality improvement metrics
Cost reduction metrics

The Ultimate Goal

Build the mobile OS.

When CORE can orchestrate LLMs to build an operating system with: - Millions of lines of code - Dozens of languages - Hundreds of components - Constitutional compliance throughout - Zero human intervention (after NorthStar approval)

Then we've proven: CORE is the last programmer you'll ever need.

Appendices

Appendix A: Glossary

Atomic Action: Indivisible unit of work with standard contract (ActionResult)

ActionResult: Universal result structure returned by all atomic actions

Constitution: Complete set of policies, patterns, and validation rules for a domain

Domain: Area of expertise (e.g., Python, kernel C, web frontend)

Hallucination: LLM-generated content that references non-existent entities

Mind-Body-Will: CORE's architectural separation of concerns

NorthStar: Document capturing user's true intent (what they mean, not what they said)

Phase: Logical grouping of related actions in a workflow

Policy: Constitutional rule that defines correctness (e.g., "imports must be grouped")

Workflow: Sequence of atomic actions organized into phases to achieve a goal

Appendix B: Key Decisions

Why ActionResult over multiple result types?

Universal governance requires universal contracts
Different types fragment the architecture
Single type enables universal tooling

Why Mind-Body-Will separation?

Humans best at defining correctness (Mind)
Machines best at execution (Body)
AI best at orchestration (Will)
Separation prevents confusion and enables specialization

Why constitutional validation instead of just testing?

Tests validate behavior, not architecture
Constitution validates design, patterns, compliance
Testing happens after generation; constitution happens during
Prevention is better than detection

Why LLM + Constitution instead of perfect LLM?

Perfect LLMs don't exist and may never exist
Bounded LLMs are achievable with current technology
Constitution as source of truth is human-controllable
Failures are constitutional events, not AI failures

Why multi-domain instead of specializing?

Real projects span domains
Specialization fragments the ecosystem
Universal governance is the innovation
Multi-domain proves the architecture scales

Appendix C: Risks and Mitigations

Risk: LLMs can't generate complex systems reliably

Mitigation:

Don't rely on single LLM calls
Break into atomic actions
Validate each action
Retry with constraints
Escalate to human if needed

Risk: Constitutional policies too rigid

Mitigation:

Policies are code (version controlled)
Policies can be updated
Multiple policy sets per project
Override mechanisms for special cases

Risk: Too complex for users

Mitigation:

Hide complexity behind dialogue
Sane defaults for everything
Progressive disclosure of options
Expert mode for power users

Risk: Performance too slow

Mitigation:

Cache LLM results
Parallel action execution
Incremental validation
Background processing

Risk: Can't compete with established tools

Mitigation:

Not competing on speed (competing on governance)
Target different use case (autonomous building vs. assisted coding)
Integrate with existing tools (don't replace)
Focus on unique value (constitutional AI)

Appendix D: Success Metrics

Technical Metrics:

Time to build complete system
Lines of code generated per hour
Constitutional compliance rate
Hallucination detection rate
Action success rate

Business Metrics:

Developer time saved
Reduction in security vulnerabilities
Reduction in compliance violations
Increase in code quality scores

Adoption Metrics:

Active users
Projects built
Domains supported
Community contributions

Vision Metrics:

Complexity of systems built
Domains in single project
Autonomy level achieved
Self-improvement iterations

Appendix E: Philosophical Foundation

Why "Last Programmer"?

Not because programmers become obsolete, but because:

CORE handles the mechanical parts (syntax, patterns, standards)
Humans focus on creative parts (intent, architecture, innovation)
The "programming" that remains is higher-level (goals, constraints, policies)

Analogy: Calculators didn't eliminate mathematicians. They eliminated tedious arithmetic, letting mathematicians focus on theory and proofs.

CORE eliminates tedious coding, letting developers focus on design and innovation.

Why Constitutional AI?

Current AI is powerful but ungoverned. Like a car with no brakes—fast but dangerous.

Constitutional AI adds the governance layer—the brakes, steering, and traffic laws that make speed safe.

Not limiting AI's capability. Directing it toward correctness.

Why Open Source?

This is too important to be proprietary:

AI governance affects everyone
Constitutional frameworks should be transparent
Community contributions improve quality
Trust requires openness

CORE's source code is open. CORE's constitution is open. CORE's future is open.

Conclusion

CORE is not a tool. CORE is not a framework. CORE is not even a product.

CORE is a vision for how AI should build systems:

Creatively (using LLMs)
Correctly (using constitutions)
Autonomously (using orchestration)
Governably (using Mind-Body-Will)

The vision is ambitious: "Build a mobile operating system."

But the path is incremental: Python → C → Kotlin → ... → Mobile OS.

Each step validates the architecture. Each domain proves the scalability. Each project demonstrates the value.

And at the end—when CORE can orchestrate LLMs to build an OS with constitutional compliance throughout—we'll have proven something profound:

AI can build anything. Constitution makes it correct. Together, they're unstoppable.

That's CORE.

The last programmer you'll ever need.

Document Status: Constitutional (Read-Only) Version: 1.0.0 Date: November 30, 2025

This document defines CORE's vision and shall remain unchanged except through formal constitutional amendment process.

CORE: The Last Programmer You Will Ever Need

Abstract

Table of Contents

The Genesis

The LIRA Project

The Insight Cascade

The Problem Space

The LLM Reliability Crisis

The Governance Gap

The Intent Translation Problem

The Multi-Domain Challenge

The Core Insight

The Fundamental Principle

The Mind-Body-Will Separation

"Do Not Code What I Say, Code What I Mean"

The Vision

The Ultimate Goal

Not a Tool—A System

The Transformation

Scope of "Everything"

The Architecture

The Layers (Detailed)

The Data Flow

Key Architectural Principles

The Execution Model

Atomic Actions (The Foundation)

Action Metadata (Constitutional Definition)

Workflow Composition

The LLM-Constitution Loop

Hallucination Detection

The Constitutional Framework

What is a Constitution?

Example Constitution: Python Domain

Example Constitution: GDPR Compliance

Domain Registry

Multi-Domain Projects

The Roadmap

Current State (A2: Constitutional Compliance)

Phase 1: Foundation

Phase 2: Dialogue System

Phase 3: Simple Autonomy

Phase 4: LIRA Rebuilt

Phase 5: Complex Multi-Domain Multi-Language(Year 2, Q1-Q2)

E-commerce Platform:

IoT System:

Phase 6: Mobile OS (Year 2, Q3-Q4)

Phase 7: Self-Improvement (Year 3+)

Why This Will Work

1. Proven Foundation

2. The Right Abstraction

3. Separation of Concerns

4. LLM Integration Strategy

5. Incremental Validation

6. Real Use Cases

7. Constitutional Governance

Comparison to Existing Solutions

GitHub Copilot

Cursor/Windsurf

Devin (Cognition AI)

AutoGPT/BabyAGI

Traditional Development Tools

The Competitive Matrix

Technical Specifications

System Requirements

Performance Targets

API Structure

Storage Schema

Security Model

The Path Forward

Immediate Next Steps

Finish ActionResult Migration (Week 1-4):

Document Current State:

First External Validation

Add Second Language (C):

Blog Post / Demo:

First Real Product

Build Something Useful:

Open Source Release:

Gather Community

Engage AI Safety Researchers: