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Multi-Cloud Deployment

Overview

ADEPT is designed for multi-cloud operation from the ground up. OCI-compliant container images, provider-neutral Helm charts, and pluggable LLM routing allow a single ADEPT deployment to span AWS, Azure, and GCP -- or federate multiple deployments across clouds using the A2A gateway mesh.

Multi-Cloud Strategy

The architecture separates concerns into three layers:

graph TD
    subgraph Application["Application Layer (Portable)"]
        HELM[Helm Chart]
        DOCKER[OCI Images]
    end
    subgraph IaC["Infrastructure Layer (Provider-Specific)"]
        CDK[AWS CDK]
        PULUMI[Azure Pulumi]
        TF[GCP Terraform]
    end
    subgraph LLM["LLM Routing Layer (Provider-Neutral)"]
        LITELLM[LiteLLM Router]
    end
    HELM --> CDK
    HELM --> PULUMI
    HELM --> TF
    Application --> LLM
Layer Portability Tool
Application Fully portable Helm + OCI images
Infrastructure Provider-specific CDK / Pulumi / Terraform
LLM Routing Provider-neutral LiteLLM abstraction

Provider Matrix

Capability AWS Azure GCP
Kubernetes EKS AKS GKE
IaC Tool CDK (Python) Pulumi (Python) Terraform (HCL)
Managed PostgreSQL RDS Flexible Server Cloud SQL
Managed Redis ElastiCache Azure Cache Memorystore
Container Registry ECR ACR Artifact Registry
Secrets Secrets Manager Key Vault Secret Manager
LLM Provider Bedrock Azure OpenAI Vertex AI
Ingress ALB App Gateway Cloud LB

LLM Provider Configuration

ADEPT routes LLM requests through LiteLLM, supporting multiple providers simultaneously. The model name prefix determines the provider:

# Azure OpenAI
DEFAULT_LLM_MODEL=azure/gpt-4o

# AWS Bedrock
DEFAULT_LLM_MODEL=bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0

# Anthropic Direct
DEFAULT_LLM_MODEL=anthropic/claude-sonnet-4-5-20250929

# Google Vertex AI
DEFAULT_LLM_MODEL=vertex_ai/gemini-2.0-flash

# Local Ollama
DEFAULT_LLM_MODEL=ollama/llama3.1

# NVIDIA NIMs
DEFAULT_LLM_MODEL=nvidia_nim/meta/llama-3.1-70b-instruct

Purpose-Based Routing

Different agent roles can use different providers. A coding agent might use AWS Bedrock Claude while the main agent uses Azure OpenAI. Configure via config/model_catalog.yaml.

Cross-Cloud Federation

The A2A (Agent-to-Agent) protocol enables ADEPT instances in different clouds to communicate:

graph LR
    subgraph AWS
        GW1[Gateway A]
    end
    subgraph Azure
        GW2[Gateway B]
    end
    subgraph GCP
        GW3[Gateway C]
    end
    REG[Gateway Registry]
    GW1 <--> REG
    GW2 <--> REG
    GW3 <--> REG
    GW1 <-->|A2A Protocol| GW2
    GW2 <-->|A2A Protocol| GW3

Each gateway registers with a shared Gateway Registry. Agents on one gateway can discover and invoke tools hosted on any federated peer, enabling cross-cloud workflows without data migration.

Deployment Patterns

Pattern 1: Single-Cloud Full Stack

Deploy all components in one cloud provider. Simplest operational model.

# AWS example
cd infra/aws/cdk && cdk deploy --all
helm install adept infra/helm/agentic-framework/ -f values-aws.yaml

Pattern 2: Multi-Cloud Federation

Deploy independent stacks per cloud, federate via A2A:

  1. Deploy Stack A in AWS (owns HPC tools)
  2. Deploy Stack B in Azure (owns Azure OpenAI models)
  3. Register both gateways in shared registry
  4. Agents in either stack can invoke tools from both

Pattern 3: Hybrid Cloud + On-Premises

Combine cloud deployments with on-premises Docker Compose instances for air-gapped or data-sovereignty requirements. The A2A mesh treats all gateways identically regardless of hosting.

Network Requirements

Cross-cloud federation requires HTTPS connectivity between gateways on port 443. Configure firewall rules, VPC peering, or VPN tunnels as appropriate for your security posture.