Deployment Infrastructure

This document outlines the deployment infrastructure and strategies for the AI Agent Orchestration Platform.

Overview

The platform is designed to be deployed in various environments, from local development to production, with support for cloud, on-premises, and hybrid deployments. This document focuses on the infrastructure components and configurations needed for successful deployment.

Deployment Environments

Local Development

Local development deployment is covered in the Infrastructure Overview document. For detailed on-premises development options, see On-Premises Development.

Staging Environment

The staging environment mirrors the production setup but with reduced resources:

• Containerized services using Docker Compose or Kubernetes
• Isolated database instance with anonymized production data
• Feature flags for testing new capabilities
• Monitoring and logging identical to production
• CI/CD pipeline integration for automated deployments

Production Environment

Production deployment focuses on reliability, scalability, and security:

• Kubernetes-based orchestration for container management
• High-availability database configuration
• Load balancing and auto-scaling
• Comprehensive monitoring and alerting
• Regular backup and disaster recovery procedures
• Security hardening and compliance measures

Deployment Architectures

Cloud Deployment

For cloud-based deployments, the platform supports:

• AWS Architecture:
• EKS for Kubernetes orchestration
• RDS for PostgreSQL database
• S3 for object storage
• CloudFront for content delivery
• CloudWatch for monitoring

• IAM for access control

• Azure Architecture:

• AKS for Kubernetes orchestration
• Azure Database for PostgreSQL
• Blob Storage for objects
• Azure CDN for content delivery
• Azure Monitor for monitoring

• Azure Active Directory for authentication

• GCP Architecture:

• GKE for Kubernetes orchestration
• Cloud SQL for PostgreSQL
• Cloud Storage for objects
• Cloud CDN for content delivery
• Cloud Monitoring for monitoring

• IAM for access control

• Cloudflare Architecture:

• Cloudflare Workers for serverless functions
• Cloudflare Pages for frontend hosting
• Cloudflare D1 for SQLite database
• Cloudflare R2 for object storage
• Cloudflare KV for key-value storage
• Cloudflare Durable Objects for stateful applications
• Cloudflare Queues for asynchronous processing

On-Premises Deployment

For on-premises deployments, the platform requires:

• Kubernetes cluster (e.g., Rancher, OpenShift)
• PostgreSQL database with high availability
• NFS or similar for shared storage
• Nginx or HAProxy for load balancing
• Prometheus and Grafana for monitoring
• LDAP or Active Directory integration

Edge Deployment

For edge computing scenarios, see Edge Infrastructure for detailed requirements.

Deployment Process

Continuous Deployment Pipeline

The platform uses a GitOps approach to deployment:

1. Code changes are pushed to the repository
2. CI pipeline runs tests and builds container images
3. Container images are tagged and pushed to registry
4. CD pipeline updates Kubernetes manifests
5. ArgoCD or Flux applies changes to the cluster
6. Monitoring confirms successful deployment

Deployment Scripts

Key deployment scripts are located in /infra/scripts/:

• deploy.sh - Main deployment script
• rollback.sh - Rollback to previous version
• health_check.sh - Verify deployment health
• migrate_db.sh - Run database migrations

Example deployment script:

#!/bin/bash
# deploy.sh - Deploy the platform to the specified environment

ENV=$1
VERSION=$2

if [ -z "$ENV" ] || [ -z "$VERSION" ]; then
  echo "Usage: ./deploy.sh [environment] [version]"
  echo "Example: ./deploy.sh staging 1.2.3"
  exit 1
fi

echo "Deploying version $VERSION to $ENV environment..."

# Update Kubernetes manifests
./update_manifests.sh $ENV $VERSION

# Apply changes
kubectl apply -f ./kubernetes/$ENV/

# Run database migrations
./migrate_db.sh $ENV

# Verify deployment
./health_check.sh $ENV

echo "Deployment complete!"

Fragmented Deployment with SST.dev

For projects requiring deployment across multiple cloud services with a single script, SST.dev provides an Infrastructure as Code (IaC) solution:

1. Single Configuration: Define all infrastructure components in TypeScript/JavaScript
2. Unified Deployment: Deploy frontend, backend, and infrastructure with a single command
3. Environment Management: Easily manage multiple environments (dev, staging, prod)

Example SST configuration for fragmented deployment:

// sst.config.ts
import { SSTConfig } from "sst";
import { API } from "./stacks/API";
import { Web } from "./stacks/Web";
import { Database } from "./stacks/Database";
import { Storage } from "./stacks/Storage";

export default {
  config(_input) {
    return {
      name: "meta-agent-platform",
      region: "us-east-1",
    };
  },
  stacks(app) {
    // Deploy specific stacks based on environment or flags
    if (process.env.DEPLOY_DB === "true") {
      app.stack(Database);
    }
    if (process.env.DEPLOY_API === "true" || !process.env.DEPLOY_API) {
      app.stack(API);
    }
    if (process.env.DEPLOY_WEB === "true" || !process.env.DEPLOY_WEB) {
      app.stack(Web);
    }
    if (process.env.DEPLOY_STORAGE === "true") {
      app.stack(Storage);
    }
  },
} satisfies SSTConfig;

Deployment script for selective component deployment:

#!/bin/bash
# fragmented-deploy.sh - Deploy specific components of the platform

ENV=$1
COMPONENTS=$2

if [ -z "$ENV" ] || [ -z "$COMPONENTS" ]; then
  echo "Usage: ./fragmented-deploy.sh [environment] [components]"
  echo "Example: ./fragmented-deploy.sh dev 'api,web'"
  exit 1
fi

echo "Deploying components $COMPONENTS to $ENV environment..."

# Set environment variables based on components
if [[ $COMPONENTS == *"api"* ]]; then
  export DEPLOY_API=true
fi

if [[ $COMPONENTS == *"web"* ]]; then
  export DEPLOY_WEB=true
fi

if [[ $COMPONENTS == *"db"* ]]; then
  export DEPLOY_DB=true
fi

if [[ $COMPONENTS == *"storage"* ]]; then
  export DEPLOY_STORAGE=true
fi

# Deploy using SST
npx sst deploy --stage $ENV

echo "Fragmented deployment complete!"

Blue-Green Deployments

For zero-downtime updates, the platform supports blue-green deployments:

1. New version (green) is deployed alongside current version (blue)
2. Green deployment is tested and verified
3. Traffic is gradually shifted from blue to green
4. Once green is receiving all traffic, blue is decommissioned

Canary Deployments

For risk mitigation, the platform supports canary deployments:

1. New version is deployed to a small subset of users/servers
2. Performance and errors are monitored
3. If metrics are acceptable, deployment continues to more users
4. If issues are detected, traffic is routed back to the stable version

Rollback Procedures

In case of deployment issues, automated rollback procedures are in place:

1. Detect issues through monitoring alerts
2. Execute rollback script to revert to previous stable version
3. Route traffic back to stable version
4. Investigate and fix issues in the problematic deployment

Deployment Configuration Management

Deployment configurations are managed using:

• Kubernetes ConfigMaps and Secrets for application configuration
• Helm charts for templating and packaging
• Kustomize for environment-specific customizations
• Sealed Secrets or Vault for sensitive information
• SST.dev configuration for serverless deployments
• Wrangler configuration for Cloudflare deployments

References

• CI/CD Pipeline
• Containerization
• Monitoring Infrastructure
• Security Infrastructure
• Scaling Strategies
• Cloudflare Deployment
• Serverless Deployment with SST
• On-Premises Development

---

Last updated: 2025-04-18