Appendix G: Technical Specifications
Complete System Architecture and Implementation Details
This appendix provides comprehensive technical specifications for reproducing, extending, and deploying AI futures analysis systems. It covers hardware requirements, software architecture, performance benchmarks, and deployment configurations.
System Architecture Overview
High-Level Architecture
┌─────────────────────────────────────────────────────────────────┐
│ AI Futures Analysis System │
├─────────────────────────────────────────────────────────────────┤
│ User Interface Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Web UI │ │ API Gateway│ │ Interactive Tools │ │
│ │ (React/JS) │ │ (FastAPI) │ │ (Jupyter/Streamlit) │ │
│ └──────────────┘ └──────────────┘ └──────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Application Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Evidence │ │ Bayesian │ │ Monte Carlo Engine │ │
│ │ Integration │ │ Processor │ │ (NumPy/Numba) │ │
│ └──────────────┘ └──────────────┘ └──────────────────────────┘ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Causal │ │ Sensitivity │ │ Visualization │ │
│ │ Network │ │ Analysis │ │ (Plotly/Matplotlib) │ │
│ └──────────────┘ └──────────────┘ └──────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Data Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Evidence │ │ Results │ │ Configuration │ │
│ │ Database │ │ Cache │ │ Management │ │
│ │ (PostgreSQL) │ │ (Redis) │ │ (YAML/JSON) │ │
│ └──────────────┘ └──────────────┘ └──────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Infrastructure Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Compute │ │ Storage │ │ Monitoring │ │
│ │ Cluster │ │ Systems │ │ (Prometheus/Grafana) │ │
│ │ (K8s/Docker)│ │ (S3/GCS) │ │ │ │
│ └──────────────┘ └──────────────┘ └──────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
Component Dependencies
graph TD
A[Evidence Database] --> B[Bayesian Processor]
B --> C[Causal Network]
C --> D[Monte Carlo Engine]
D --> E[Results Cache]
E --> F[Visualization]
E --> G[API Gateway]
F --> H[Web UI]
G --> H
I[Configuration] --> B
I --> C
I --> D
J[Monitoring] --> K[All Components]
Hardware Specifications
Development Environment
Minimum Requirements:
CPU:
Cores: 4
Clock: 2.5 GHz
Architecture: x86_64 or ARM64
Memory:
RAM: 8 GB
Swap: 4 GB
Storage:
Type: SSD
Space: 100 GB available
IOPS: 1000+ (for database operations)
Network:
Bandwidth: 100 Mbps
Latency: <50ms to data sources
Recommended Configuration:
CPU:
Cores: 8-16
Clock: 3.0+ GHz
Architecture: x86_64
Features: AVX2, FMA support for NumPy optimization
Memory:
RAM: 32 GB DDR4-3200
Swap: 8 GB
Storage:
Primary: 1 TB NVMe SSD (OS and applications)
Data: 2 TB SSD or fast HDD (data storage)
IOPS: 10,000+ (NVMe recommended)
Graphics:
GPU: Optional but recommended for visualization
VRAM: 4+ GB for large plot rendering
Production Environment:
CPU:
Cores: 16-32 per node
Clock: 3.5+ GHz
Architecture: x86_64 with AVX-512
Memory:
RAM: 64-128 GB per node
ECC: Recommended for mission-critical deployments
Storage:
Type: Enterprise NVMe SSD
Capacity: 5+ TB per node
IOPS: 50,000+ per node
Replication: RAID 10 or distributed storage
Network:
Bandwidth: 10+ Gbps between nodes
Latency: <1ms inter-node communication
Scaling Characteristics
CPU Scaling:
def cpu_scaling_efficiency(cores):
"""Theoretical scaling efficiency by core count"""
if cores <= 4:
return 0.95 # Near-linear scaling
elif cores <= 8:
return 0.85 # Good scaling with some overhead
elif cores <= 16:
return 0.70 # Moderate scaling, I/O limits
else:
return 0.50 # Poor scaling, memory bandwidth limits
# Performance scaling formula
performance = base_performance * cores * cpu_scaling_efficiency(cores)
Memory Requirements by Problem Size:
Evidence Sources:
100 sources: 1 GB RAM
500 sources: 3 GB RAM
1000 sources: 6 GB RAM
5000 sources: 25 GB RAM
Monte Carlo Iterations:
1M iterations: 2 GB RAM
10M iterations: 8 GB RAM
100M iterations: 32 GB RAM
1B iterations: 128 GB RAM
Scenarios:
64 scenarios: Base requirement
128 scenarios (7 hypotheses): 2x RAM
256 scenarios (8 hypotheses): 4x RAM
Software Requirements
Core Dependencies
Python Environment:
Python: 3.9.0 - 3.11.x
Package Manager: pip 21.0+ or conda 4.10+
Core Packages:
numpy: ">=1.21.0"
scipy: ">=1.7.0"
pandas: ">=1.3.0"
matplotlib: ">=3.4.0"
seaborn: ">=0.11.0"
networkx: ">=2.6.0"
numba: ">=0.54.0"
scikit-learn: ">=1.0.0"
Statistical Packages:
pymc3: ">=3.11.0" # Bayesian analysis
arviz: ">=0.11.0" # Bayesian visualization
SALib: ">=1.4.0" # Sensitivity analysis
Web Framework:
fastapi: ">=0.70.0"
uvicorn: ">=0.15.0"
streamlit: ">=1.0.0" # Interactive tools
Visualization:
plotly: ">=5.0.0"
bokeh: ">=2.4.0"
holoviews: ">=1.14.0"
System Dependencies:
# Ubuntu/Debian
sudo apt-get update
sudo apt-get install -y \
build-essential \
python3-dev \
libopenblas-dev \
liblapack-dev \
gfortran \
pkg-config \
libhdf5-dev
# CentOS/RHEL
sudo yum groupinstall -y "Development Tools"
sudo yum install -y \
python3-devel \
openblas-devel \
lapack-devel \
gcc-gfortran \
pkgconfig \
hdf5-devel
# macOS
brew install openblas lapack gcc hdf5
Database Requirements:
PostgreSQL:
Version: ">=12.0"
Extensions:
- uuid-ossp
- pg_stat_statements
Configuration:
shared_preload_libraries: 'pg_stat_statements'
max_connections: 200
shared_buffers: 256MB
effective_cache_size: 1GB
Redis:
Version: ">=6.0"
Configuration:
maxmemory: 2GB
maxmemory-policy: allkeys-lru
save: "900 1 300 10 60 10000"
Container Specifications
Docker Configuration:
FROM python:3.9-slim
# System dependencies
RUN apt-get update && apt-get install -y \
build-essential \
libopenblas-dev \
liblapack-dev \
gfortran \
&& rm -rf /var/lib/apt/lists/*
# Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Application code
WORKDIR /app
COPY . .
# Resource limits
ENV PYTHONUNBUFFERED=1
ENV OMP_NUM_THREADS=4
ENV NUMBA_NUM_THREADS=4
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s \
CMD curl -f http://localhost:8000/health || exit 1
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
Kubernetes Deployment:
apiVersion: apps/v1
kind: Deployment
metadata:
name: ai-futures-api
spec:
replicas: 3
selector:
matchLabels:
app: ai-futures-api
template:
metadata:
labels:
app: ai-futures-api
spec:
containers:
- name: api
image: ai-futures:latest
ports:
- containerPort: 8000
resources:
limits:
cpu: "4"
memory: "8Gi"
requests:
cpu: "2"
memory: "4Gi"
env:
- name: DATABASE_URL
valueFrom:
secretKeyRef:
name: db-secret
key: url
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 30
periodSeconds: 10
Performance Benchmarks
Computation Performance
Monte Carlo Performance by Hardware:
Intel i7-12700K (12 cores, 32GB RAM):
1M iterations: 0.8 seconds
10M iterations: 7.2 seconds
100M iterations: 68 seconds
1B iterations: 11.5 minutes
AMD Threadripper 3970X (32 cores, 64GB RAM):
1M iterations: 0.3 seconds
10M iterations: 2.1 seconds
100M iterations: 19 seconds
1B iterations: 3.2 minutes
AWS c5.4xlarge (16 vCPUs, 32GB RAM):
1M iterations: 1.2 seconds
10M iterations: 10.1 seconds
100M iterations: 95 seconds
1B iterations: 16.8 minutes
Memory Usage Patterns:
def estimate_memory_usage(scenarios, iterations, evidence_count):
"""Estimate peak memory usage in GB"""
base_memory = 0.5 # Base Python overhead
# Evidence storage
evidence_memory = evidence_count * 0.001 # ~1MB per 1000 evidence pieces
# Monte Carlo arrays
scenario_memory = scenarios * iterations * 8 / (1024**3) # 8 bytes per float
# Network computation
network_memory = scenarios * scenarios * 8 / (1024**3) # Adjacency matrix
# Visualization buffers
viz_memory = max(2.0, scenarios * 0.01) # At least 2GB for plots
total = base_memory + evidence_memory + scenario_memory + network_memory + viz_memory
return round(total, 2)
# Example calculations
print(f"Standard analysis (64 scenarios, 5M iterations, 120 evidence): {estimate_memory_usage(64, 5_000_000, 120)} GB")
print(f"Extended analysis (256 scenarios, 10M iterations, 500 evidence): {estimate_memory_usage(256, 10_000_000, 500)} GB")
Optimization Techniques:
# NumPy vectorization example
def optimized_probability_calculation(scenarios, weights):
"""Vectorized probability calculation - 100x faster than loops"""
return np.dot(scenarios.T, weights) / np.sum(weights)
# Numba JIT compilation
@numba.jit(nopython=True)
def monte_carlo_step(params):
"""JIT-compiled simulation step - 50x faster than pure Python"""
# Implementation details...
return result
# Memory-efficient chunked processing
def process_large_dataset(data, chunk_size=10000):
"""Process data in chunks to avoid memory overflow"""
for i in range(0, len(data), chunk_size):
chunk = data[i:i+chunk_size]
yield process_chunk(chunk)
Database Performance
PostgreSQL Configuration for Performance:
-- Optimize for analytical workloads
ALTER SYSTEM SET shared_buffers = '4GB';
ALTER SYSTEM SET effective_cache_size = '12GB';
ALTER SYSTEM SET maintenance_work_mem = '1GB';
ALTER SYSTEM SET checkpoint_completion_target = 0.9;
ALTER SYSTEM SET wal_buffers = '64MB';
ALTER SYSTEM SET default_statistics_target = 100;
-- Indexes for evidence queries
CREATE INDEX idx_evidence_hypothesis ON evidence (hypothesis);
CREATE INDEX idx_evidence_quality ON evidence (overall_quality);
CREATE INDEX idx_evidence_date ON evidence (publication_date);
CREATE INDEX idx_evidence_composite ON evidence (hypothesis, overall_quality, publication_date);
Query Performance Benchmarks:
Evidence Retrieval (120 sources):
Simple select: <1ms
Quality filtered: 2-5ms
Complex aggregation: 10-20ms
Results Storage (64 scenarios × 26 years):
Insert batch: 50-100ms
Update probabilities: 20-50ms
Temporal queries: 5-15ms
Full Analysis Pipeline:
Evidence integration: 500ms - 2s
Monte Carlo simulation: 30s - 5min
Result storage: 1-5s
Visualization generation: 5-30s
Configuration Management
Environment Configuration
Development Environment (.env):
# Database
DATABASE_URL=postgresql://user:pass@localhost:5432/ai_futures_dev
REDIS_URL=redis://localhost:6379/0
# Computation
MAX_WORKERS=4
MONTE_CARLO_ITERATIONS=1000000
CHUNK_SIZE=10000
ENABLE_JIT=true
# API
DEBUG=true
LOG_LEVEL=INFO
CORS_ORIGINS=["http://localhost:3000", "http://localhost:8080"]
# Caching
CACHE_TTL=3600
ENABLE_RESULT_CACHE=true
Production Environment:
# Database (use environment secrets management)
DATABASE_URL=${DB_CONNECTION_STRING}
DATABASE_POOL_SIZE=20
DATABASE_MAX_OVERFLOW=40
# Computation
MAX_WORKERS=16
MONTE_CARLO_ITERATIONS=10000000
ENABLE_DISTRIBUTED=true
CLUSTER_NODES=3
# Security
SECRET_KEY=${SECRET_KEY}
ALLOWED_HOSTS=["api.aifutures.org", "aifutures.org"]
ENABLE_HTTPS=true
# Monitoring
PROMETHEUS_ENDPOINT=http://prometheus:9090
LOG_LEVEL=WARNING
SENTRY_DSN=${SENTRY_DSN}
Application Configuration
Core Parameters (config.yaml):
analysis:
hypotheses:
count: 6
labels: ["H1", "H2", "H3", "H4", "H5", "H6"]
descriptions:
H1: "AI Progress"
H2: "AGI Achievement"
H3: "Employment Impact"
H4: "Safety Outcomes"
H5: "Development Model"
H6: "Governance Response"
evidence:
quality_threshold: 0.4
max_age_years: 10
replication_bonus: 0.2
authority_weight: 0.3
methodology_weight: 0.3
recency_weight: 0.2
replication_weight: 0.2
monte_carlo:
default_iterations: 5000000
max_iterations: 100000000
convergence_threshold: 0.001
random_seed: 42
causal_network:
max_iterations: 10
convergence_epsilon: 1e-6
strength_multiplier: 1.0
enable_feedback_loops: true
computation:
parallel_processing:
enable: true
max_workers: null # auto-detect
chunk_size: 10000
optimization:
enable_jit: true
use_numba: true
vectorize_operations: true
memory_limit_gb: null # auto-detect
output:
precision: 4
scientific_notation: false
export_formats: ["json", "csv", "parquet"]
visualization_dpi: 300
Deployment Configurations
Docker Compose for Development:
version: '3.8'
services:
api:
build: .
ports:
- "8000:8000"
environment:
- DATABASE_URL=postgresql://postgres:password@db:5432/ai_futures
- REDIS_URL=redis://redis:6379/0
depends_on:
- db
- redis
volumes:
- ./data:/app/data
- ./logs:/app/logs
db:
image: postgres:13
environment:
POSTGRES_DB: ai_futures
POSTGRES_USER: postgres
POSTGRES_PASSWORD: password
ports:
- "5432:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
redis:
image: redis:6-alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
frontend:
image: node:16
working_dir: /app
volumes:
- ./frontend:/app
ports:
- "3000:3000"
command: npm run dev
volumes:
postgres_data:
redis_data:
Production Kubernetes Configuration:
# ConfigMap for application settings
apiVersion: v1
kind: ConfigMap
metadata:
name: ai-futures-config
data:
config.yaml: |
analysis:
monte_carlo:
default_iterations: 10000000
evidence:
quality_threshold: 0.6
computation:
parallel_processing:
max_workers: 16
---
# Deployment with resource limits
apiVersion: apps/v1
kind: Deployment
metadata:
name: ai-futures-production
spec:
replicas: 5
selector:
matchLabels:
app: ai-futures
tier: production
template:
metadata:
labels:
app: ai-futures
tier: production
spec:
containers:
- name: api
image: ai-futures:v1.2.0
resources:
limits:
cpu: "8"
memory: "16Gi"
requests:
cpu: "4"
memory: "8Gi"
volumeMounts:
- name: config-volume
mountPath: /app/config
- name: data-volume
mountPath: /app/data
volumes:
- name: config-volume
configMap:
name: ai-futures-config
- name: data-volume
persistentVolumeClaim:
claimName: ai-futures-data
API Specifications
REST API Endpoints
Core Analysis Endpoints:
POST /api/v1/analysis/run:
description: Run complete AI futures analysis
parameters:
- name: iterations
type: integer
default: 5000000
- name: evidence_filter
type: object
properties:
quality_min: float
sources: array[string]
hypothesis: string
responses:
200:
content:
application/json:
schema:
type: object
properties:
scenario_probabilities: object
temporal_evolution: array
computation_time: float
metadata: object
GET /api/v1/scenarios:
description: List all scenarios with probabilities
parameters:
- name: min_probability
type: float
default: 0.01
- name: sort_by
type: string
enum: [probability, scenario_id, cluster]
responses:
200:
content:
application/json:
schema:
type: array
items:
type: object
properties:
scenario_id: string
probability: float
cluster: string
description: string
POST /api/v1/sensitivity:
description: Run sensitivity analysis
parameters:
- name: parameters
type: array
items: string
- name: method
type: string
enum: [sobol, morris, local]
responses:
200:
content:
application/json:
schema:
type: object
properties:
first_order: object
total_order: object
interactions: object
Evidence Management Endpoints:
GET /api/v1/evidence:
description: Retrieve evidence database
parameters:
- name: hypothesis
type: string
- name: min_quality
type: float
- name: limit
type: integer
responses:
200:
content:
application/json:
schema:
type: object
properties:
evidence: array
total_count: integer
filters_applied: object
POST /api/v1/evidence:
description: Add new evidence
requestBody:
content:
application/json:
schema:
type: object
properties:
hypothesis: string
direction: string
strength: float
quality_scores: object
source: string
description: string
responses:
201:
description: Evidence added successfully
400:
description: Validation error
WebSocket Interfaces
Real-time Analysis Updates:
// Connect to analysis progress updates
const ws = new WebSocket('ws://api.example.com/ws/analysis/progress');
ws.onmessage = function(event) {
const data = JSON.parse(event.data);
switch(data.type) {
case 'progress':
updateProgressBar(data.completed, data.total);
break;
case 'result':
displayResults(data.scenarios, data.probabilities);
break;
case 'error':
handleError(data.message);
break;
}
};
// Start analysis
ws.send(JSON.stringify({
action: 'start_analysis',
parameters: {
iterations: 1000000,
hypotheses: ['H1', 'H2', 'H3', 'H4', 'H5', 'H6']
}
}));
Security Specifications
Authentication and Authorization
API Authentication:
from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
import jwt
app = FastAPI()
security = HTTPBearer()
def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
try:
payload = jwt.decode(credentials.credentials, SECRET_KEY, algorithms=["HS256"])
return payload
except jwt.InvalidTokenError:
raise HTTPException(status_code=401, detail="Invalid token")
@app.get("/api/v1/analysis", dependencies=[Depends(verify_token)])
async def get_analysis():
# Protected endpoint
pass
Role-Based Access Control:
roles:
analyst:
permissions:
- read:evidence
- read:scenarios
- create:analysis
admin:
permissions:
- read:*
- write:*
- delete:evidence
viewer:
permissions:
- read:scenarios
- read:results
rate_limits:
analyst:
analysis_runs: 10/hour
evidence_queries: 100/hour
viewer:
scenario_queries: 50/hour
Data Protection
Encryption Configuration:
encryption:
at_rest:
algorithm: AES-256-GCM
key_rotation: 90_days
in_transit:
protocol: TLS 1.3
certificate_authority: Let's Encrypt
database:
enable_encryption: true
transparent_data_encryption: true
Privacy Controls:
def anonymize_sensitive_data(data):
"""Remove or hash sensitive information"""
sensitive_fields = ['email', 'ip_address', 'user_id']
for field in sensitive_fields:
if field in data:
data[field] = hash_field(data[field])
return data
def apply_data_retention(records, retention_days=730):
"""Remove records older than retention period"""
cutoff_date = datetime.now() - timedelta(days=retention_days)
return [r for r in records if r.created_at > cutoff_date]
Monitoring and Observability
Metrics Collection
Application Metrics:
from prometheus_client import Counter, Histogram, Gauge
import time
# Custom metrics
analysis_runs_total = Counter('analysis_runs_total', 'Total analysis runs')
analysis_duration = Histogram('analysis_duration_seconds', 'Analysis duration')
active_analyses = Gauge('active_analyses', 'Currently running analyses')
def monitor_analysis(func):
def wrapper(*args, **kwargs):
active_analyses.inc()
analysis_runs_total.inc()
start_time = time.time()
try:
result = func(*args, **kwargs)
return result
finally:
analysis_duration.observe(time.time() - start_time)
active_analyses.dec()
return wrapper
Infrastructure Monitoring:
metrics:
system:
- cpu_usage_percent
- memory_usage_bytes
- disk_io_operations
- network_bytes_total
application:
- http_requests_total
- http_request_duration_seconds
- database_connections_active
- cache_hit_ratio
business:
- analysis_runs_daily
- evidence_sources_count
- scenario_calculations_total
- user_sessions_active
alerts:
high_cpu_usage:
condition: cpu_usage_percent > 80
duration: 5m
severity: warning
analysis_errors:
condition: analysis_error_rate > 0.05
duration: 1m
severity: critical
database_slow_queries:
condition: database_query_duration_p95 > 1s
duration: 2m
severity: warning
Logging Configuration
Structured Logging:
import logging
import json
from datetime import datetime
class StructuredLogger:
def __init__(self):
self.logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
formatter = logging.Formatter('%(message)s')
handler.setFormatter(formatter)
self.logger.addHandler(handler)
self.logger.setLevel(logging.INFO)
def log_analysis_start(self, analysis_id, parameters):
self.logger.info(json.dumps({
'event': 'analysis_started',
'analysis_id': analysis_id,
'timestamp': datetime.utcnow().isoformat(),
'parameters': parameters
}))
def log_analysis_complete(self, analysis_id, duration, results):
self.logger.info(json.dumps({
'event': 'analysis_completed',
'analysis_id': analysis_id,
'timestamp': datetime.utcnow().isoformat(),
'duration_seconds': duration,
'scenario_count': len(results)
}))
Deployment Procedures
Production Deployment Checklist
Pre-deployment:
- Code review completed
- Unit tests passing (>95% coverage)
- Integration tests passing
- Security scan completed
- Performance benchmarks met
- Database migrations tested
- Configuration reviewed
- Monitoring alerts configured
Deployment Steps:
#!/bin/bash
# Production deployment script
set -e # Exit on any error
echo "Starting production deployment..."
# 1. Backup current system
kubectl create backup production-backup-$(date +%Y%m%d-%H%M%S)
# 2. Apply database migrations
python manage.py migrate --check
python manage.py migrate
# 3. Update configuration
kubectl apply -f k8s/configmap.yaml
# 4. Rolling deployment
kubectl set image deployment/ai-futures-api api=ai-futures:${BUILD_VERSION}
kubectl rollout status deployment/ai-futures-api
# 5. Health checks
./scripts/health_check.sh
# 6. Smoke tests
./scripts/smoke_test.sh
echo "Deployment completed successfully!"
Post-deployment:
- Health checks passing
- Smoke tests completed
- Performance metrics normal
- Error rates within limits
- User acceptance testing
- Rollback plan confirmed
Disaster Recovery
Backup Strategy:
backups:
database:
frequency: daily
retention: 30_days
verification: weekly
location: multiple_regions
application_data:
frequency: hourly
retention: 7_days
incremental: true
configuration:
frequency: on_change
retention: 90_days
version_control: true
recovery_procedures:
rto: 4_hours # Recovery Time Objective
rpo: 1_hour # Recovery Point Objective
automated_failover:
enable: true
health_check_interval: 30s
failure_threshold: 3
Recovery Testing:
#!/bin/bash
# Disaster recovery drill script
echo "Starting disaster recovery drill..."
# Simulate primary failure
kubectl scale deployment/ai-futures-api --replicas=0
# Activate backup systems
kubectl apply -f k8s/disaster-recovery/
# Restore from backup
./scripts/restore_backup.sh ${LATEST_BACKUP}
# Verify functionality
./scripts/full_system_test.sh
echo "Disaster recovery drill completed"
This comprehensive technical specification provides the foundation for implementing, deploying, and maintaining AI futures analysis systems at any scale. Regular updates to these specifications ensure optimal performance and reliability as the system evolves.