A CAIO-Level Architectural Specification

HOW THIS DOCUMENT RELATES TO OTHER ARCHITECTURE DOCS

Document	Focus	View
This doc (40)	10 System-Level Components	What the systems ARE (architecture)
`15_protocol_foundation_and_build_sequence.md`	Build Sequence	WHEN to build (temporal)
`37_engineering_composition_12_components.md`	13 Buildable Components	WHAT to build (work items)
`11_engineering_blueprint.md`	Protocol Foundation → Products	HOW it fits together

These are complementary views, not competing specifications.

HUMAN is not an app, database, or centralized platform.

It is a distributed identity, capability, and orchestration protocol composed of:

HUMΛ̊N Passport — portable human-owned identity + encrypted personal data vault
Capability Graph Protocol (CGP) — composable, verifiable capability ontology
HumanOS — real-time orchestration engine
HUMAN Ledger — cryptographic attestation + provenance layer
Workforce Cloud Runtime (WFCR) — the task-routing and execution substrate
Academy Engine — learning → capability → work compilers
HUMAN Device Runtime (HDR) — identity + consent module on consumer hardware
Enterprise Integration Layer (EIL) — gateways, APIs, SDKs
AI Partnership Fabric (APF) — controls, boundaries, and collaboration channels
Observability & Safety Layer — global audit, simulation, red-team pipelines

Everything is modular, cryptographically anchored, device-first, and non-capturable.

No HUMAN system can operate without the person in the loop.

Mapping to Build Sequence

System (This Doc)	Foundation (Week 1-2)	Wave 1 (Week 3-6)	Wave 2+
Passport	✅ Passport-Lite	—	Full Passport
Capability Graph	✅ Capability-Lite	—	Full CGP
HumanOS	✅ Routing-Lite	—	Full HumanOS
Ledger	—	—	✅ Full Ledger
Workforce Cloud	—	✅ AI Labs API	Full WFCR
Academy Engine	—	✅ Academy Foundation	Full Academy
Device Runtime	—	—	✅ HDR
Enterprise Layer	—	—	✅ EIL
AI Partnership	—	✅ LLM Integration	Full APF
Observability	✅ Provenance-Lite	—	Full Safety Layer

See: 15_protocol_foundation_and_build_sequence.md for the canonical build sequence.

HUMAN PASSPORT (THE PROTOCOL)

Core Requirements

The Passport must:

Be portable
Be decryptable only by the human
Persist across devices via key-sharded recovery
Store structured + unstructured personal data
Contain cryptographic pointers to attestations
Support revocable consent envelopes
Be verifiable offline and online
Never require HUMAN servers for identity resolution

Passport Architecture

Data never lives "in HUMAN." Only proofs do.

PassportContainer {
    DID: decentralized_identifier
    PK: public_key
    RecoveryShards: [device_shards, optional_delegate_shards]
    DataVaultPointer: encrypted_location
    CapabilityGraphRoot: CGP_pointer
    ConsentLeases: list<consent_contract>
    LedgerRefs: list<attestation_hash>
}

Device Runtime (HDR)

Runs inside:

Secure Enclave / TEE (Apple SEP, Android StrongBox)
WebAuthn-capable browsers
WatchOS secure element
Future devices

Functionality:

Key generation
Local decryption
Prompting for consent
Signing for proof-of-possession
Verifying attestation certificates
Local reasoning sandbox (small ML models)

HDR is the heart of portability.

"HUMAN lives on the device; not in the cloud."

HUMAN LEDGER (THE CRYPTOGRAPHIC TRUST LAYER)

The Ledger is not a blockchain in the "store everything forever" sense.

It is:

A minimalist distributed log
Optimized for identity proofs, attestation hashes, revocation, and capability events
With a cryptographic quorum-of-quorums consensus
And geographic + organizational distribution

Ledger Responsibilities

Issue decentralized identity proofs
Timestamp capability updates
Anchor Academy completions
Hold revocation and permission logs
Provide cross-network attestation equivalence
Support zero-knowledge queries (e.g., "prove capability X without revealing Y")

What is Stored On-Ledger?

Only:

hashes
timestamps
attestation signatures
revocation markers
consent tokens
routing metadata

Not:

PII
payload
raw capability data

CAPABILITY GRAPH PROTOCOL (CGP)

Purpose

The Capability Graph creates a verifiable, portable representation of:

decisions
demonstrated behaviors
training completions
validated outcomes
judgments
risk signals
learning progression
emotional/relational competencies
human-only skills
hybrid human+AI roles

Graph Model

Graph is a DAG where:

Nodes = capability signatures
Edges = provenance links
Weights = depth/strength of demonstrated proficiency
Layers = conceptual domains (cognitive, social, procedural, contextual, safety)

CGP_Node {
    id,
    domain,
    capability_type,
    evidence_pointers[],
    ledger_signature,
    confidence,
    freshness_window,
    visibility_policy
}

Evidence Model

Evidence is stored off-chain in:

encrypted personal vaults
enterprise attestations
Academy completions
workflow logs (zero-knowledge transformed)
human reviewers' checks
AI-review signals verified by HumanOS

Each piece of evidence receives:

cryptographic hash
issuance signature
revocation hooks
visibility contract
expiry metadata

Auto-Growth Mechanism

Capability Graph grows via:

training → capability compiler output
work tasks → decision signatures
AI collaboration → human override signals
peer attestation → multi-party trust anchors
self-improvement → micro-capability accumulation

No scoring.
No ranking.
Only representation.

ACADEMY ENGINE (LEARNING → WORK COMPILER)

Purpose

Transforms learning into capability
and capability into real work.

Architecture

Pipeline:

Curriculum → Microtasks → Demonstrations → Evaluation → Attestation → CGP update → Work routing

Key Components

Adaptive Learning Planner (ALP)

personalized pathways
dynamic curriculum adjustment

Capability Compiler (CC)

transforms training → CGP nodes
generates attestations

Simulation Engine

scenario-based behavior capture
edge case training

AI Tutor Mesh

agents specialized by domain
adaptive teaching

Human Evaluator Loop

high-stakes verification
quality assurance

Safety Module

filters for integrity
gaming prevention

Revenue Integration

Training tasks = paid micro-work:

AI labeling
review cycles
safety checks
nuance analysis

Training is:

free for humans
monetized by selling value back to enterprises + AI labs

HUMANOS (THE ORCHESTRATION LAYER)

Purpose

Decide:

which human
with what capability
under what constraints
should intervene
in which AI-driven workflow
and under what responsibility model

Inputs

HumanOS consumes:

Passport claims
Capability Graph states
Workflow metadata
AI system intent
Risk profiles
Context signals
Task semantics
Legal constraints
Human preferences & boundaries

Decision Engine Architecture

Uses:

Typed workflow DAGs
Capability matchers
Constraint solvers
Risk stratification models
Explainability engines
Human escalation graphs

Flow:

AI action → HumanOS preflight check →
    if SAFE:
        allow + log
    if UNCERTAIN:
        route to human
    if DANGEROUS:
        escalation → specialist

Guarantees

HumanOS guarantees:

No human is assigned unsafe tasks
No AI executes irreversible actions without human clearance
Every decision has provenance
Every workflow is auditable
Every escalation has justification

WORKFORCE CLOUD RUNTIME

Purpose

Deliver human capability into AI-driven workflows.

Runtime Modules

Task Router
Performance Guardrails
Risk Monitor
Trust Tiering System
Compensation Engine
Micro-workflow Compiler
Enterprise SLA Manager

Data Boundaries

Workforce Cloud never receives raw identity.

It receives:

capability signatures
task context
consent receipt
temporary routing token

ENTERPRISE INTEGRATION LAYER (EIL)

APIs

Identity API

verifiable credential exchange

Capability Query API

CGP queries with ZK-proofs

HumanOS Decision API

routing external tasks

Workforce Cloud API

staffing requests

Attestation API

enterprise-to-ledger publishing

SDKs

Java
TypeScript
Swift
Go
Python

Built for: hospitals, governments, logistics systems, LLM vendors.

AI PARTNERSHIP FABRIC (APF)

Purpose

Give AI the rules of engagement.

AI should know:

what it is allowed to do
what it must escalate
what is outside its boundaries
what requires human review
what must be logged

APF Components

Policy interpreter
Risk assessor
Human override monitor
Safety-layer API adapters
Fine-grained capability checks

OBSERVABILITY, SAFETY, AND SIMULATION LAYER

Components

Safety Metrics Hub

Real-time safety monitoring

Decision Provenance Graph (aka “Context Graph”)

Decision traceability (“why did this happen?”) across entities and time
Built from ProvenanceRecords + Attestations emitted in the execution path
Includes: routing decisions, approvals, overrides, escalations, safety events, and outcomes

Note: earlier drafts called this the “Causality Graph.” The canonical term is Decision Provenance Graph to match HumanOS and API language.

Counterfactual Simulator

"What if" scenario testing

Red-teaming Arena

Adversarial testing

Global Audit Log

Comprehensive provenance

Event Slicer

Investigation tools

Guarantees

End-to-end verifiability
Reconstruction of any decision
"Why did this happen?" traceability
Zero dark operations

BUILD ORDER (0 → 6 MONTHS)

Month 0–1

CI/CD
core DIDs
HDR prototype
Ledger v0
CGP schema
Enrollment flow (MVP)

Month 2–3

Academy Engine v0
Capability Compiler
Simulation sandbox
Ledger v1
Consent envelopes

Month 4–5

HumanOS v0
Workforce Cloud v0
Enterprise API sandbox
End-to-end identity → training → capability → work pipeline

Month 6

First live enterprise pilot
Workforce Cloud revenue
Public launch of Passport beta
Onboarding 500–2,000 early testers

TECHNICAL PRINCIPLES

1. Device-First, Cloud-Second

Identity lives on devices, not servers.

2. Cryptography Over Policy

Trust is mathematical, not reputational.

3. Distributed Over Centralized

No single point of control or failure.

4. Privacy by Architecture

Cannot collect what isn't designed to be collected.

5. Modular Over Monolithic

Each component independently deployable.

6. Open Over Proprietary

Standard specifications, multiple implementations possible.

7. Human Override Always Available

No AI decision is irreversible without human approval.

8. Explainability by Default

Every system decision must be traceable and understandable.

9. Neutral by Design

No vendor, government, or corporation can capture the protocol.

10. Future-Proof

Design for 100-year protocol lifespan.

DATABASE & DATA LAYER ARCHITECTURE

Core Design Principles

1. PostgreSQL as Foundation

Single source of truth for structured data
ACID guarantees for identity and capability data
pgvector extension for semantic search
Proven at scale (billions of rows, petabytes of data)

2. Connection Pooling Required

PgBouncer in transaction mode
Prevents connection exhaustion under load
25 connections to PostgreSQL, 1000 max clients
Per-process pools: 4 Fastify (20 each) + 2 BullMQ workers (10 each)

3. Partition for High-Volume Tables

Provenance events: Range partitioned by month
Enables efficient queries on historical data
Drop/archive old partitions without table locks
Automated partition creation for ongoing operations

4. Index Strategy

Index all foreign keys (JOIN performance)
Composite indexes for multi-column queries
GIN indexes for JSONB columns
HNSW indexes for vector similarity (better recall <10K docs)
Partial indexes for soft-deleted records

5. Query Timeouts

10s for API queries (prevent runaway queries)
5min for background jobs
idle_in_transaction_session_timeout: 60s
Protects database from long-running transactions

Schema Design Patterns

Capability Graph Tables:

CREATE TABLE capability_nodes (
  id UUID PRIMARY KEY,
  passport_did TEXT NOT NULL,
  name TEXT NOT NULL,
  weight NUMERIC(3,2) CHECK (weight >= 0 AND weight <= 1),
  evidence_count INT DEFAULT 0,
  deleted_at TIMESTAMPTZ,  -- Soft delete
  UNIQUE(passport_did, name)
);

CREATE TABLE capability_evidence (
  id UUID PRIMARY KEY,
  capability_node_id UUID REFERENCES capability_nodes(id),
  evidence_type TEXT NOT NULL,
  metadata JSONB NOT NULL,
  recorded_at TIMESTAMPTZ DEFAULT NOW()
);

-- Critical indexes for routing
CREATE INDEX idx_capability_nodes_name_weight 
  ON capability_nodes(name, weight DESC) 
  WHERE deleted_at IS NULL;

Provenance Table (Partitioned):

CREATE TABLE provenance_events (
  id UUID NOT NULL,
  event_type VARCHAR(50) NOT NULL,
  actor_did VARCHAR(100) NOT NULL,
  context JSONB NOT NULL,
  created_at TIMESTAMPTZ NOT NULL
) PARTITION BY RANGE (created_at);

-- Monthly partitions
CREATE TABLE provenance_events_2025_12 
  PARTITION OF provenance_events
  FOR VALUES FROM ('2025-12-01') TO ('2026-01-01');

KB Documents with Vector Search:

CREATE TABLE kb_documents (
  id UUID PRIMARY KEY,
  file_path VARCHAR(500) UNIQUE NOT NULL,
  content TEXT NOT NULL,
  embedding VECTOR(3072),  -- text-embedding-3-large
  governance_tier VARCHAR(20) NOT NULL,
  classification VARCHAR(30) NOT NULL,
  indexed_at TIMESTAMPTZ DEFAULT NOW()
);

-- HNSW for better recall at small scale
CREATE INDEX idx_kb_embedding ON kb_documents 
  USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 64);

Scaling Strategy

Phase	Trigger	Action	Timeline
Single Instance	Launch	PostgreSQL 15+ + PgBouncer	Week 3
Read Replica	Users > 2K OR P95 > 150ms	Add streaming replica	Month 2
Partitioning	Provenance > 10M rows	Already built-in	Day 1
Sharding Plan	Capability nodes > 1M	Document strategy	Month 4
Sharding	P95 > 200ms OR disk > 1TB	Implement by passport_did	Month 6+

Sharding Key Selection:

Shard by passport_did (high-cardinality, stable)
Single-shard queries: Get/update user capabilities
Multi-shard queries: Find all users with capability X (scatter-gather)

Query Performance Targets:

Service	P95 Latency	P99 Latency
Passport queries	<150ms	<300ms
Capability routing	<200ms	<400ms
KB semantic search	<100ms	<200ms
Provenance logging	<50ms	<100ms

Backup & Recovery

RPO (Recovery Point Objective): 1 hour

Daily full snapshots (retained 30 days)
Hourly WAL archiving
Point-in-time recovery (last 7 days)

RTO (Recovery Time Objective): 4 hours

Restore from snapshot: ~2 hours
Replay WAL logs: ~1 hour
Validation and cutover: ~1 hour

Disaster Recovery:

Multi-region replication (Month 6+)
Automated failover with health checks
Monthly restore drills to validate backup integrity

Monitoring & Observability

pg_stat_statements:

-- Identify slow queries
SELECT query, mean_exec_time / 1000 AS mean_seconds
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 10;

-- Find missing indexes (sequential scans)
SELECT tablename, seq_scan, idx_scan
FROM pg_stat_user_tables
WHERE seq_scan > idx_scan
ORDER BY seq_scan DESC;

Connection Pool Health:

// Monitor pool metrics
pool.on('connect', () => metrics.increment('db.pool.connections.active'));
pool.on('error', (err) => metrics.increment('db.pool.errors'));

setInterval(() => {
  metrics.gauge('db.pool.total', pool.totalCount);
  metrics.gauge('db.pool.idle', pool.idleCount);
  metrics.gauge('db.pool.waiting', pool.waitingCount);
}, 10000);

Data Integrity Guarantees

1. Foreign Key Constraints

All relationships enforced at database level
CASCADE deletes documented and audited
Prevent orphaned records

2. Check Constraints

Enum validation (governance_tier, classification)
Range validation (weight 0.0-1.0)
Required field enforcement (NOT NULL)

3. Unique Constraints

One capability per person: UNIQUE(passport_did, name)
One DID per user: UNIQUE(did)
Automatic index creation

4. Soft Deletes

Use deleted_at TIMESTAMPTZ instead of hard deletes
Partial indexes exclude deleted records
Provenance trail preserved

5. Transactional Updates

Use database transactions for multi-step operations
Capability + evidence updates are atomic
Rollback on any failure

Cross-References

Complete DB Schema: kb/124_human_v01_production_prd.md (Section 1.2, 2.2.6, 4.1.4)
Capability Graph Schema: kb/21_capability_graph_engine.md (Capability-Lite Database Schema)
KB Vector Search: kb/10_ai_internal_use_and_companion_spec.md (Database Performance Optimizations)
Performance Guide: kb/102_performance_engineering_guide.md (when created)

WHY THIS ARCHITECTURE IS DEFENSIBLE

No one else can build this because:

Big Tech cannot be neutral
Their incentive is platform lock-in, not protocol openness.

Governments cannot be global
National solutions don't scale across borders.

Crypto projects lack enterprise credibility
Speculation undermines trust.

AI labs lack identity expertise
They build models, not identity protocols.

HR platforms lack AI orchestration
They manage résumés, not capability.

HUMAN sits at the intersection of all these domains.

Metadata

Source Sections:

Lines 25,402-25,803: SECTION 50 — THE HUMAN TECHNICAL BLUEPRINT v1.0

Merge Strategy: COMPREHENSIVE EXTRACTION - Preserved all unique technical architecture detail

Strategic Purposes:

Building (PRIMARY)
Companion

Cross-References:

See: 11_engineering_blueprint.md - Complementary engineering architecture
See: 20_passport_identity_layer.md - Detailed Passport spec
See: 21_capability_graph_engine.md - Detailed Graph spec
See: 22_humanos_orchestration_core.md - Detailed HumanOS spec
See: 15_protocol_foundation_and_build_sequence.md - Build sequence

v0.1 Production References:

See: 124_human_v01_production_prd.md - Complete v0.1 PRD with production specs
See: 122_global_scale_architecture_v01.md - v0.1 deployment architecture
See: 123_v01_launch_readiness_checklist.md - Launch go/no-go criteria

Line Count: ~900 lines (comprehensive extraction from ~402 line source with expanded technical detail)
Extracted: November 24, 2025
Updated: December 8, 2025
Version: 2.1 (Added v0.1 Production References)

40. THE HUMAN TECHNICAL BLUEPRINT v1.0

HOW THIS DOCUMENT RELATES TO OTHER ARCHITECTURE DOCS

Mapping to Build Sequence

HUMAN PASSPORT (THE PROTOCOL)

Core Requirements

Passport Architecture

Device Runtime (HDR)

HUMAN LEDGER (THE CRYPTOGRAPHIC TRUST LAYER)

Ledger Responsibilities

What is Stored On-Ledger?

CAPABILITY GRAPH PROTOCOL (CGP)

Purpose

Graph Model

Evidence Model

Auto-Growth Mechanism

ACADEMY ENGINE (LEARNING → WORK COMPILER)

Purpose

Architecture

Key Components

Revenue Integration

HUMANOS (THE ORCHESTRATION LAYER)

Purpose

Inputs

Decision Engine Architecture

Guarantees

WORKFORCE CLOUD RUNTIME

Purpose

Runtime Modules

Data Boundaries

ENTERPRISE INTEGRATION LAYER (EIL)

APIs

SDKs

AI PARTNERSHIP FABRIC (APF)

Purpose

APF Components

OBSERVABILITY, SAFETY, AND SIMULATION LAYER

Components

Guarantees

BUILD ORDER (0 → 6 MONTHS)

Month 0–1

Month 2–3

Month 4–5

Month 6

TECHNICAL PRINCIPLES

1. Device-First, Cloud-Second

2. Cryptography Over Policy

3. Distributed Over Centralized

4. Privacy by Architecture

5. Modular Over Monolithic

6. Open Over Proprietary

7. Human Override Always Available

8. Explainability by Default

9. Neutral by Design

10. Future-Proof

DATABASE & DATA LAYER ARCHITECTURE

Core Design Principles

Schema Design Patterns

Scaling Strategy

Backup & Recovery

Monitoring & Observability

Data Integrity Guarantees

Cross-References

WHY THIS ARCHITECTURE IS DEFENSIBLE

Metadata