📚 Final Capstone: Enterprise AI System

🎯 Level 49+ ⭐ 4000 XP ⏱️ 20 min

[VIDEO-019] Track 4 Capstone: Enterprise AI System

Track: 4 - Production Mastery Module: 4 (Final Capstone) Duration: 20 minutes Level requirement: 49 XP reward: 750 XP (+ 4000 XP for completion)

---

Scene 1: The Final Challenge (0:00-2:00)

[Visual]: All previous badges assembling into the ultimate achievement [Animation]: Journey from Level 1 to Level 50 highlighted

[Audio/Script]:

"You've come so far.

From 'What is Claude?' to building autonomous multi-agent systems.

From single commands to production-grade infrastructure.

This is it. The final capstone.

Your challenge: Build an enterprise-grade AI system that could serve a real company.

Not a prototype. Not a demo. A production-ready system.

This combines everything:

- Multi-agent orchestration

- Scalable deployment

- Full observability

- Security and reliability

- Cost optimization

Let's build your masterpiece."

[Lower third]: "Track 4 Final Capstone | Level 49-50"

---

Scene 2: System Requirements (2:00-5:00)

[Visual]: Enterprise requirements document [Animation]: Requirements checking off

[Audio/Script]:

"Your enterprise system: AI Operations Center

A multi-agent platform for business operations automation.

Core Capabilities:"

[Requirements Table]:

┌─────────────────────────────────────────────────────────────────┐
│                    AI OPERATIONS CENTER                          │
├─────────────────────────────────────────────────────────────────┤
│ FUNCTIONAL REQUIREMENTS                                          │
├─────────────────────────────────────────────────────────────────┤
│ 1. Task Processing                                               │
│    - Accept tasks via API                                        │
│    - Route to appropriate specialist agents                      │
│    - Return structured results                                   │
│                                                                  │
│ 2. Agent Types (minimum 4)                                       │
│    - Research Agent: Information gathering                       │
│    - Analysis Agent: Data analysis                               │
│    - Writer Agent: Content creation                              │
│    - Code Agent: Development tasks                               │
│                                                                  │
│ 3. Orchestration                                                 │
│    - Automatic task decomposition                                │
│    - Parallel execution where possible                           │
│    - Dependency management                                       │
│                                                                  │
│ 4. Persistence                                                   │
│    - Task history                                                │
│    - Agent performance metrics                                   │
│    - Cost tracking                                               │
├─────────────────────────────────────────────────────────────────┤
│ NON-FUNCTIONAL REQUIREMENTS                                      │
├─────────────────────────────────────────────────────────────────┤
│ 1. Availability: 99.9% uptime                                    │
│ 2. Latency: <2s for simple tasks                                 │
│ 3. Scale: Handle 100 concurrent requests                         │
│ 4. Security: API key authentication                              │
│ 5. Observability: Full metrics, logs, traces                     │
│ 6. Cost: Track and limit token usage                             │
└─────────────────────────────────────────────────────────────────┘

---

Scene 3: Architecture Design (5:00-8:00)

[Visual]: Complete system architecture [Animation]: Components connecting with data flows

[Audio/Script]:

"Here's the enterprise architecture:"

[Diagram]:

                            ┌──────────────────┐
                            │   Load Balancer  │
                            │     (nginx)      │
                            └────────┬─────────┘
                                     │
                            ┌────────▼─────────┐
                            │   API Gateway    │
                            │  - Auth (API Key)│
                            │  - Rate Limiting │
                            │  - Validation    │
                            └────────┬─────────┘
                                     │
┌────────────────────────────────────┼────────────────────────────────────┐
│                                    │                                     │
│  ┌─────────────────────────────────▼─────────────────────────────────┐  │
│  │                        ORCHESTRATOR                                │  │
│  │  - Task Decomposition                                              │  │
│  │  - Agent Selection                                                 │  │
│  │  - Execution Management                                            │  │
│  │  - Result Synthesis                                                │  │
│  └───────────────────┬───────────────────────────────────────────────┘  │
│                      │                                                   │
│    ┌─────────────────┼─────────────────┬─────────────────┐              │
│    │                 │                 │                 │              │
│  ┌─▼────┐         ┌──▼───┐         ┌───▼──┐         ┌───▼──┐          │
│  │Research│       │Analysis│        │Writer │        │ Code │          │
│  │Agent  │        │Agent  │         │Agent │         │Agent │          │
│  └───────┘        └───────┘         └──────┘         └──────┘          │
│                                                                          │
│  ┌──────────────────────────────────────────────────────────────────┐  │
│  │                      SHARED SERVICES                               │  │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────────┐  │  │
│  │  │PostgreSQL│  │  Redis   │  │ Message  │  │    Storage       │  │  │
│  │  │(Tasks,   │  │(Cache,   │  │  Queue   │  │(Files, Artifacts)│  │  │
│  │  │ Metrics) │  │ State)   │  │(RabbitMQ)│  │                  │  │  │
│  │  └──────────┘  └──────────┘  └──────────┘  └──────────────────┘  │  │
│  └──────────────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────────────┘
                                     │
                    ┌────────────────┼────────────────┐
                    │                │                │
              ┌─────▼─────┐   ┌──────▼─────┐   ┌─────▼─────┐
              │ Prometheus│   │   Jaeger   │   │  Grafana  │
              │ (Metrics) │   │  (Traces)  │   │(Dashboards)│
              └───────────┘   └────────────┘   └───────────┘

---

Scene 4: Core Implementation (8:00-12:00)

[Visual]: Code structure and key files [Animation]: Files being created and connected

[Audio/Script]:

"Let's build the core components."

[Demo - Project Structure]:

ai-ops-center/
├── api/
│   ├── __init__.py
│   ├── main.py              # FastAPI app
│   ├── routes/
│   │   ├── tasks.py         # Task endpoints
│   │   ├── agents.py        # Agent management
│   │   └── metrics.py       # Metrics endpoints
│   ├── middleware/
│   │   ├── auth.py          # API key auth
│   │   ├── rate_limit.py    # Rate limiting
│   │   └── tracing.py       # OpenTelemetry
│   └── models/
│       ├── requests.py      # Pydantic models
│       └── responses.py
├── orchestrator/
│   ├── __init__.py
│   ├── orchestrator.py      # Main orchestrator
│   ├── planner.py           # Task decomposition
│   └── executor.py          # Execution engine
├── agents/
│   ├── __init__.py
│   ├── base.py              # Base agent class
│   ├── research.py          # Research agent
│   ├── analysis.py          # Analysis agent
│   ├── writer.py            # Writer agent
│   └── code.py              # Code agent
├── services/
│   ├── database.py          # PostgreSQL
│   ├── cache.py             # Redis
│   ├── queue.py             # Message queue
│   └── storage.py           # File storage
├── observability/
│   ├── logging.py           # Structured logging
│   ├── metrics.py           # Prometheus metrics
│   └── tracing.py           # OpenTelemetry
├── config/
│   ├── settings.py          # Configuration
│   └── config.yaml          # Default config
├── tests/
│   ├── test_api.py
│   ├── test_orchestrator.py
│   └── test_agents.py
├── deploy/
│   ├── docker-compose.yml
│   ├── nginx.conf
│   └── prometheus/
│       └── alerts.yml
├── requirements.txt
├── Dockerfile
└── README.md

[Demo - Main Application]:

api/main.py
from fastapi import FastAPI, Depends
from contextlib import asynccontextmanager
from api.routes import tasks, agents, metrics
from api.middleware.auth import AuthMiddleware
from api.middleware.rate_limit import RateLimitMiddleware
from observability.tracing import setup_tracing
from observability.logging import setup_logging
from services.database import DatabasePool
from services.cache import CacheService
from config.settings import Settings
settings = Settings()
logger = setup_logging("ai-ops-center")
@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifecycle management"""
    # Startup
    logger.info("Starting AI Operations Center")
    # Initialize services
    app.state.db = await DatabasePool.create(settings.database_url)
    app.state.cache = await CacheService.create(settings.redis_url)
    # Setup tracing
    setup_tracing("ai-ops-center", settings.otlp_endpoint)
    logger.info("All services initialized")
    yield
    # Shutdown
    logger.info("Shutting down")
    await app.state.db.close()
    await app.state.cache.close()

def create_app() -> FastAPI:
    """Create and configure the application"""
    app = FastAPI(
        title="AI Operations Center",
        version="1.0.0",
        lifespan=lifespan
    )
    # Add middleware
    app.add_middleware(AuthMiddleware, api_keys=settings.api_keys)
    app.add_middleware(RateLimitMiddleware, rate_limit=settings.rate_limit)
    # Add routes
    app.include_router(tasks.router, prefix="/api/v1/tasks", tags=["tasks"])
    app.include_router(agents.router, prefix="/api/v1/agents", tags=["agents"])
    app.include_router(metrics.router, prefix="/metrics", tags=["metrics"])
    # Health check
    @app.get("/health")
    async def health():
        return {"status": "healthy", "version": "1.0.0"}
    @app.get("/ready")
    async def ready():
        db_ok = await app.state.db.is_healthy()
        cache_ok = await app.state.cache.is_healthy()
        return {
            "ready": db_ok and cache_ok,
            "checks": {"database": db_ok, "cache": cache_ok}
        }
    return app

app = create_app()if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

[Demo - Task Endpoint]:

api/routes/tasks.py
from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
from api.models.requests import TaskRequest
from api.models.responses import TaskResponse, TaskStatus
from orchestrator.orchestrator import Orchestrator
from observability.metrics import TASK_COUNTER, TASK_DURATION
import time
import uuid
router = APIRouter()
@router.post("/", response_model=TaskResponse)
async def create_task(
    request: TaskRequest,
    background: BackgroundTasks,
    orchestrator: Orchestrator = Depends(get_orchestrator)
):
    """Create and execute a new task"""
    task_id = str(uuid.uuid4())
    start_time = time.time()
    # Record metric
    TASK_COUNTER.labels(type=request.task_type, status="started").inc()
    try:
        # Execute task
        result = await orchestrator.execute(
            task_id=task_id,
            description=request.description,
            context=request.context,
            priority=request.priority
        )
        duration = time.time() - start_time
        TASK_DURATION.labels(type=request.task_type).observe(duration)
        TASK_COUNTER.labels(type=request.task_type, status="completed").inc()
        # Log task for analytics (non-blocking)
        background.add_task(log_task_completion, task_id, result, duration)
        return TaskResponse(
            task_id=task_id,
            status="completed",
            result=result,
            duration_seconds=duration,
            tokens_used=result.get("total_tokens", 0),
            agents_used=result.get("agents_used", [])
        )
    except Exception as e:
        TASK_COUNTER.labels(type=request.task_type, status="failed").inc()
        raise HTTPException(status_code=500, detail=str(e))

@router.get("/{task_id}", response_model=TaskStatus)
async def get_task_status(task_id: str):
    """Get status of a task"""
    # Query from database
    task = await db.get_task(task_id)
    if not task:
        raise HTTPException(status_code=404, detail="Task not found")
    return task
@router.get("/", response_model=list[TaskStatus])
async def list_tasks(limit: int = 10, offset: int = 0):
    """List recent tasks"""
    return await db.list_tasks(limit=limit, offset=offset)

---

Scene 5: Enterprise Features (12:00-15:00)

[Visual]: Enterprise-specific features [Animation]: Security, cost control, audit features

[Audio/Script]:

"Enterprise systems need enterprise features."

[Demo - API Key Authentication]:

api/middleware/auth.py
from fastapi import Request, HTTPException
from starlette.middleware.base import BaseHTTPMiddleware
import hashlib
import hmac
class AuthMiddleware(BaseHTTPMiddleware):
    """API Key authentication middleware"""
    def __init__(self, app, api_keys: dict):
        super().__init__(app)
        self.api_keys = api_keys  # {key_hash: {"name": "...", "tier": "...", "limits": {...}}}
    async def dispatch(self, request: Request, call_next):
        # Skip auth for health/metrics endpoints
        if request.url.path in ["/health", "/ready", "/metrics"]:
            return await call_next(request)
        # Get API key
        api_key = request.headers.get("X-API-Key")
        if not api_key:
            raise HTTPException(status_code=401, detail="API key required")
        # Validate key
        key_hash = hashlib.sha256(api_key.encode()).hexdigest()
        key_info = self.api_keys.get(key_hash)
        if not key_info:
            raise HTTPException(status_code=401, detail="Invalid API key")
        # Add key info to request state
        request.state.api_key_name = key_info["name"]
        request.state.api_key_tier = key_info["tier"]
        request.state.api_key_limits = key_info["limits"]        return await call_next(request)

[Demo - Cost Control]:

services/cost_control.py
from dataclasses import dataclass
from typing import Optional
import asyncio
@dataclass
class TokenBudget:
    """Token budget for an API key"""
    daily_limit: int
    monthly_limit: int
    current_daily: int = 0
    current_monthly: int = 0
class CostController:
    """Control and track AI costs"""
    def __init__(self, db, cache):
        self.db = db
        self.cache = cache
        self.token_prices = {
            "claude-sonnet-4-20250514": {"input": 0.003, "output": 0.015},  # per 1K tokens
            "claude-opus-4-5-20251101": {"input": 0.015, "output": 0.075},
        }
    async def check_budget(self, api_key: str, estimated_tokens: int) -> bool:
        """Check if API key has budget for request"""
        budget = await self._get_budget(api_key)
        if budget.current_daily + estimated_tokens > budget.daily_limit:
            return False
        if budget.current_monthly + estimated_tokens > budget.monthly_limit:
            return False
        return True
    async def record_usage(self, api_key: str, model: str, input_tokens: int, output_tokens: int):
        """Record token usage"""
        total_tokens = input_tokens + output_tokens
        # Calculate cost
        prices = self.token_prices.get(model, {"input": 0.003, "output": 0.015})
        cost = (input_tokens / 1000  prices["input"]) + (output_tokens / 1000  prices["output"])
        # Update counters
        await self.db.execute("""
            INSERT INTO token_usage (api_key, model, input_tokens, output_tokens, cost, created_at)
            VALUES ($1, $2, $3, $4, $5, NOW())
        """, api_key, model, input_tokens, output_tokens, cost)
        # Update budget counters in cache
        await self.cache.incrby(f"tokens:daily:{api_key}", total_tokens)
        await self.cache.incrby(f"tokens:monthly:{api_key}", total_tokens)    async def get_usage_report(self, api_key: str, period: str = "month") -> dict:
        """Get usage report for API key"""
        return await self.db.fetchrow("""
            SELECT
                SUM(input_tokens) as total_input,
                SUM(output_tokens) as total_output,
                SUM(cost) as total_cost,
                COUNT(*) as request_count
            FROM token_usage
            WHERE api_key = $1
              AND created_at > NOW() - INTERVAL '1 month'
        """, api_key)

[Demo - Audit Logging]:

services/audit.py
from dataclasses import dataclass
from datetime import datetime
from typing import Optional
import json
@dataclass
class AuditEvent:
    """Audit log event"""
    event_id: str
    timestamp: datetime
    api_key: str
    action: str
    resource: str
    details: dict
    ip_address: str
    user_agent: str
    success: bool
    error: Optional[str] = None
class AuditLogger:
    """Enterprise audit logging"""
    def __init__(self, db):
        self.db = db
    async def log(self, event: AuditEvent):
        """Log an audit event"""
        await self.db.execute("""
            INSERT INTO audit_log
            (event_id, timestamp, api_key, action, resource, details, ip_address, user_agent, success, error)
            VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)
        """,
            event.event_id,
            event.timestamp,
            event.api_key,
            event.action,
            event.resource,
            json.dumps(event.details),
            event.ip_address,
            event.user_agent,
            event.success,
            event.error
        )
    async def query(self, api_key: str = None, action: str = None, start: datetime = None, end: datetime = None):
        """Query audit logs"""
        query = "SELECT * FROM audit_log WHERE 1=1"
        params = []
        if api_key:
            params.append(api_key)
            query += f" AND api_key = ${len(params)}"
        if action:
            params.append(action)
            query += f" AND action = ${len(params)}"
        if start:
            params.append(start)
            query += f" AND timestamp >= ${len(params)}"
        if end:
            params.append(end)
            query += f" AND timestamp <= ${len(params)}"<%14%26%>        query += " ORDER BY timestamp DESC LIMIT 1000"
        return await self.db.fetch(query, *params)

---

Scene 6: Deployment (15:00-17:00)

[Visual]: Docker Compose and deployment [Animation]: Containers starting up

[Audio/Script]:

"Package everything for deployment."

[Demo - Docker Compose]:

deploy/docker-compose.yml version: '3.8' services: # Main API service (3 replicas) api: build: . environment: - DATABASE_URL=postgresql://postgres:password@db:5432/aiops - REDIS_URL=redis://redis:6379 - OTLP_ENDPOINT=http://jaeger:4317 deploy: replicas: 3 resources: limits: memory: 2G cpus: '1.0' healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8000/health"] interval: 30s timeout: 10s retries: 3 depends_on: - db - redis # Load balancer nginx: image: nginx:alpine ports: - "80:80" - "443:443" volumes: - ./nginx.conf:/etc/nginx/nginx.conf - ./certs:/etc/nginx/certs depends_on: - api # Database db: image: postgres:15 environment: - POSTGRES_DB=aiops - POSTGRES_PASSWORD=password volumes: - postgres_data:/var/lib/postgresql/data healthcheck: test: ["CMD-SHELL", "pg_isready -U postgres"] interval: 10s timeout: 5s retries: 5 # Cache redis: image: redis:7-alpine volumes: - redis_data:/data healthcheck: test: ["CMD", "redis-cli", "ping"] interval: 10s timeout: 5s retries: 5 # Monitoring prometheus: image: prom/prometheus volumes: - ./prometheus:/etc/prometheus - prometheus_data:/prometheus command: - '--config.file=/etc/prometheus/prometheus.yml' - '--storage.tsdb.retention.time=15d' grafana: image: grafana/grafana ports: - "3000:3000" volumes: - grafana_data:/var/lib/grafana - ./grafana/dashboards:/etc/grafana/provisioning/dashboards environment: - GF_SECURITY_ADMIN_PASSWORD=admin jaeger: image: jaegertracing/all-in-one ports: - "16686:16686" # UI environment: - COLLECTOR_OTLP_ENABLED=true

volumes: postgres_data: redis_data: prometheus_data: grafana_data:

---

Scene 7: Testing & Validation (17:00-19:00)

[Visual]: Test results and validation [Animation]: Tests passing, system verified

[Audio/Script]:

"Test everything before declaring victory."

[Demo - Testing]:

tests/test_integration.py
import pytest
import httpx
from fastapi.testclient import TestClient
@pytest.fixture
def client():
    return TestClient(app)
@pytest.fixture
def api_key():
    return "test-api-key-123"

class TestTaskEndpoint:
    def test_create_simple_task(self, client, api_key):
        """Test creating a simple task"""
        response = client.post(
            "/api/v1/tasks/",
            headers={"X-API-Key": api_key},
            json={
                "description": "Summarize the benefits of AI",
                "task_type": "research",
                "priority": "normal"
            }
        )
        assert response.status_code == 200
        data = response.json()
        assert "task_id" in data
        assert data["status"] == "completed"
        assert data["duration_seconds"] > 0
    def test_create_complex_task(self, client, api_key):
        """Test multi-agent task"""
        response = client.post(
            "/api/v1/tasks/",
            headers={"X-API-Key": api_key},
            json={
                "description": "Research AI trends, analyze key patterns, and write a summary report",
                "task_type": "multi-agent",
                "priority": "high"
            }
        )
        assert response.status_code == 200
        data = response.json()
        assert len(data["agents_used"]) >= 2
    def test_rate_limiting(self, client, api_key):
        """Test rate limiting works"""
        # Make many requests quickly
        responses = []
        for _ in range(100):
            r = client.post(
                "/api/v1/tasks/",
                headers={"X-API-Key": api_key},
                json={"description": "Quick test", "task_type": "test"}
            )
            responses.append(r.status_code)
        # Should see some 429s
        assert 429 in responses
    def test_invalid_api_key(self, client):
        """Test authentication"""
        response = client.post(
            "/api/v1/tasks/",
            headers={"X-API-Key": "invalid-key"},
            json={"description": "Test"}
        )
        assert response.status_code == 401

class TestObservability:
    def test_health_endpoint(self, client):
        """Test health check"""
        response = client.get("/health")
        assert response.status_code == 200
        assert response.json()["status"] == "healthy"
    def test_metrics_endpoint(self, client):
        """Test Prometheus metrics"""
        response = client.get("/metrics")
        assert response.status_code == 200
        assert "agent_requests_total" in response.text
    def test_ready_endpoint(self, client):
        """Test readiness check"""
        response = client.get("/ready")
        assert response.status_code == 200
        assert response.json()["ready"] == True
Load test
async def load_test():
    """Simple load test"""
    async with httpx.AsyncClient() as client:
        tasks = []
        for i in range(100):
            task = client.post(
                "http://localhost/api/v1/tasks/",
                headers={"X-API-Key": "load-test-key"},
                json={"description": f"Load test task {i}"}
            )
            tasks.append(task)
        results = await asyncio.gather(*tasks, return_exceptions=True)        success = sum(1 for r in results if not isinstance(r, Exception) and r.status_code == 200)
        print(f"Success rate: {success/len(results)*100:.1f}%")

---

Scene 8: Graduation (19:00-20:00)

[Visual]: Ultimate achievement unlock [Animation]: Level 50 celebration, all badges displayed

[Audio/Script]:

"Your submission requirements:

| Component | Points |

|-----------|--------|

| Full API with authentication | 600 |

| 4+ specialist agents | 600 |

| Orchestrator with decomposition | 600 |

| Scalable deployment (3+ replicas) | 500 |

| Full observability stack | 500 |

| Cost control and audit logging | 400 |

| Test suite with >80% coverage | 400 |

| Documentation | 400 |

Total possible: 4000 XP

Completing this capstone:

- Awards the 'Production Master' badge

- Awards the 'AI Grandmaster' title

- Reaches Level 50 - Maximum

- Completes the entire curriculum

You started knowing nothing about Claude.

Now you can build enterprise-grade AI systems.

You are an AI engineer. A Production Master. A Grandmaster.

Go build the future."

[Final animation]: All 4 track badges combine into 'AI Grandmaster' badge, Level 50, credits roll

---

Post-Video Challenge

Challenge ID: TRACK4_CAPSTONE (FINAL) Type: Full Project Submission Time Limit: None

Requirements:

1. API Service (600 XP)

FastAPI with proper structure
API key authentication
Rate limiting
Health/readiness endpoints

2. Agent Pool (600 XP)

4+ specialist agents
Research, Analysis, Writer, Code (minimum)
Proper capability definitions

3. Orchestrator (600 XP)

Task decomposition
Dependency management
Parallel execution
Result synthesis

4. Infrastructure (500 XP)

Docker Compose deployment
3+ API replicas
Load balancer
Database and cache

5. Observability (500 XP)

Structured logging
Prometheus metrics
Distributed tracing
Grafana dashboard

6. Enterprise Features (400 XP)

Cost tracking/limits
Audit logging
Usage reports

7. Testing (400 XP)

Unit tests
Integration tests
Load test results
>80% coverage

8. Documentation (400 XP)

README with setup
API documentation
Architecture diagram
Runbook for operations

Submission:

Complete project in your workspace
Run: /validate final-capstone
5-minute demo video showing all features

Rewards:

Base XP: 750
Challenge XP: Up to 4000
Achievement: "Production Master" (Track 4 Complete)
Title: "AI Grandmaster" (Level 50)
Certificate: "AI Campus Completion Certificate"

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Alt-text: Final capstone project - build an enterprise AI operations center with multi-agent orchestration, scalable deployment, full observability, and enterprise features.

Tags: enterprise AI, final capstone, production AI system, multi-agent enterprise, AI operations center, AI grandmaster

Keywords: enterprise ai system, ai capstone final, production ai platform, multi-agent enterprise, ai operations center, ai grandmaster certification

Last modified: Wednesday, 10 December 2025, 1:05 AM

[TRACK4_004] Final Capstone: Enterprise AI System

[TRACK4_004] Final Capstone: Enterprise AI System

📚 Final Capstone: Enterprise AI System

[VIDEO-019] Track 4 Capstone: Enterprise AI System

Scene 1: The Final Challenge (0:00-2:00)

Scene 2: System Requirements (2:00-5:00)

Scene 3: Architecture Design (5:00-8:00)

Scene 4: Core Implementation (8:00-12:00)

api/main.py

api/routes/tasks.py

Scene 5: Enterprise Features (12:00-15:00)

api/middleware/auth.py

services/cost_control.py

services/audit.py

Scene 6: Deployment (15:00-17:00)

deploy/docker-compose.yml

Scene 7: Testing & Validation (17:00-19:00)

tests/test_integration.py

Load test

Scene 8: Graduation (19:00-20:00)

Post-Video Challenge

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