ANTIGRAVITY ENGINE ARCHITECTURE
Agentic AI Enterprise Curriculum & Tri-Cloud Blueprints
Production-grade learning modules and cloud-native blueprints covering MCP, Multi-Agent Orchestration, GraphRAG, and Tri-Cloud Deployment across Azure, AWS, and GCP.
1. EXECUTIVE SUMMARY & CORE ARCHITECTURE
Module 1: Model Context Protocol (MCP) Architecture
The Model Context Protocol (MCP) is an open specification that standardizes how AI applications (hosts/clients) securely expose tools, dynamic resources, and prompt templates from downstream enterprise systems (servers). By decoupling tool definition from LLM runtimes, MCP enables plug-and-play interoperability under Zero Trust security.
2. TRI-CLOUD ARCHITECTURAL BLUEPRINT MATRIX
| Component | Azure Implementation | AWS Implementation | GCP Implementation |
|---|---|---|---|
| Agent Runtime | Azure AI Agent Service (AutoGen) | AWS Bedrock Agents MCP Host | Vertex AI Reasoning Engine Host |
| Context & Memory | Azure AI Search (Hybrid + Semantic) | Bedrock Knowledge Bases (OpenSearch) | Vertex AI Search & Cloud Firestore |
| Tool Execution | Azure Container Apps (Microservices) | AWS Lambda / ECS Fargate | GCP Cloud Run v2 (Serverless) |
| Security & Governance | Entra ID + Azure AI Content Safety | AWS IAM + Bedrock Guardrails | GCP IAM + Vertex Model Armor |
3. TYPE-SAFE PRODUCTION CODE (PYTHON 3.11+)
Pydantic v2 + OpenTelemetryimport asyncio, logging
from pydantic import BaseModel, Field
from mcp.server import Server, NotificationOptions
from mcp.server.models import InitializationOptions
class DatabaseQueryInput(BaseModel):
query_name: str = Field(..., description="Pre-approved query template")
parameters: dict = Field(default_factory=dict)
tenant_id: str = Field(...)
server = Server("enterprise-data-mcp-server")
APPROVED_QUERIES = {
"get_customer_metrics": "SELECT customer_id, total_spend FROM analytics WHERE tenant_id = :tenant_id"
}
@server.call_tool()
async def handle_call_tool(name: str, arguments: dict):
input_data = DatabaseQueryInput.model_validate(arguments)
if input_data.query_name not in APPROVED_QUERIES:
raise PermissionError("Unapproved query execution attempt.")
return [{"type": "text", "text": "Execution Success"}]4. ENTERPRISE DEFENSE-IN-DEPTH GUARDRAILS
- Dual-LLM prompt injection defense filtering raw tool payloads
- Parameterized query templates preventing arbitrary SQL injection
- Strict execution caps: max 5 tool loops and 4096 token limit
5. SENIOR ARCHITECT EDGE-CASE CHALLENGE
Handle active-active cross-region failover between GCP us-central1 and Azure eastus without breaking tenant transaction locks.
Full evaluation criteria, idempotency guarantees, and circuit-breaker code details are available in the master blueprint file.