Introduction to Cloud#
This guide covers essential AWS services used in multi-agent systems and RAG (Retrieval-Augmented Generation) pipelines. Each service plays a specific role in building scalable, intelligent applications.
Compute & Container Services#
Amazon ECR (Elastic Container Registry)#
ECR is a managed container image registry that stores Docker images for your applications. It integrates seamlessly with EC2 and other AWS services, allowing you to version and deploy containerized agents and pipeline components.
Role in RAG/Multi-Agent Systems:
Stores Docker images for agent services
Enables easy deployment of containerized components
Manages versions of pipeline processing containers
Integrates with CI/CD for automated deployments
Amazon EC2 (Elastic Compute Cloud)#
EC2 provides virtual servers where your application code runs. In multi-agent systems and RAG pipelines, EC2 instances host the orchestration layer and agent runtime environments. You can scale up or down based on demand, paying only for the compute resources you use.
Role in RAG/Multi-Agent Systems:
Runs agent orchestration servers
Hosts background workers processing tasks
Executes inference pipelines for agent reasoning
Provides flexible compute for varying workloads
Networking Services#
NAT Gateway (Network Address Translation)#
NAT Gateway allows resources inside your private VPC to initiate outbound connections to the internet while remaining unreachable from the internet. This is crucial for secure agent operations.
Role in RAG/Multi-Agent Systems:
Enables agents to call external APIs securely
Allows vector database updates from private networks
Permits outbound API calls to LLMs or data sources
Maintains security by hiding internal infrastructure
VPC (Virtual Private Cloud)#
A VPC is a private network in AWS where you can isolate your resources. All your services (EC2, databases, etc.) run within a VPC, controlling who can access what.
Role in RAG/Multi-Agent Systems:
Isolates your agent infrastructure for security
Controls network traffic between services
Enables private communication between components
Supports compliance and data protection requirements
Storage & Data Services#
Amazon DynamoDB#
DynamoDB is a fast, NoSQL database excellent for applications requiring quick, unpredictable access patterns. It automatically scales based on traffic.
Role in RAG/Multi-Agent Systems:
Stores agent conversation state and context
Maintains session information for multi-turn interactions
Caches frequently accessed embeddings
Tracks agent decision history
Manages temporary working memory for complex tasks
Provides low-latency access to agent metadata
Amazon RDS (Relational Database Service)#
RDS provides managed relational databases (PostgreSQL, MySQL, etc.). Unlike DynamoDB, RDS is best for structured data with complex relationships and transactions.
Role in RAG/Multi-Agent Systems:
Stores structured agent configurations
Manages user profiles and permissions
Maintains audit trails of agent actions
Handles complex queries across related data
Supports ACID transactions for critical operations
Can store vector extensions (like pgvector for PostgreSQL)
Amazon S3 (Simple Storage Service)#
S3 is object storage that can hold any type of data—documents, images, logs, or raw training data. It’s highly scalable and cost-effective for large-scale data storage.
Role in RAG/Multi-Agent Systems:
Stores source documents for RAG retrieval
Holds training data for fine-tuning models
Archives conversation history and logs
Serves as a data lake for multi-agent knowledge
Enables batch processing of large datasets
Amazon S3 Vectors#
S3 can store pre-computed vector embeddings in a structured format. These embeddings represent the semantic meaning of your documents, enabling efficient similarity search and retrieval.
Role in RAG/Multi-Agent Systems:
Stores vectorized documents for fast retrieval
Enables semantic search across knowledge base
Reduces need for real-time embedding computation
Supports efficient document similarity matching
Facilitates knowledge base versioning
AI/ML Services#
Amazon Bedrock#
Amazon Bedrock provides serverless access to foundation models, enabling both language generation and embedding tasks. It simplifies the integration of advanced AI capabilities into your applications without managing infrastructure.
Role in RAG/Multi-Agent Systems:
Powers agent reasoning and decision-making with NOVA PRO
Generates responses based on retrieved context
Orchestrates multi-step agent workflows
Processes natural language instructions
Enables intelligent text generation for agent outputs
Converts documents into embeddings for RAG
Creates vector representations of user queries
Enables semantic similarity matching
Supports vector-based document retrieval
Reduces dependency on external embedding services
Security & Access Control#
AWS IAM (Identity and Access Management)#
IAM controls who can access which AWS resources and what actions they can perform. It’s fundamental for secure multi-agent architectures.
Role in RAG/Multi-Agent Systems:
Controls access to S3 storage and databases
Manages permissions for EC2 instances and containers
Defines roles for different agent services
Enforces least-privilege access principles
Enables secure communication between services
Audits all access and actions for compliance
How These Services Work Together#
In a typical RAG or multi-agent system:
Data Ingestion: Raw documents are stored in S3 and processed by Textract to extract text
Vectorization: The extracted text is converted to embeddings using Bedrock Embedding (Titan v2) and stored in S3 or DynamoDB
Agent Infrastructure: EC2 instances (containerized via ECR) run the orchestration layer within a secure VPC
External Access: NAT Gateway enables secure outbound connections for API calls
LLM Integration: Agents use Bedrock NOVA PRO for reasoning and decision-making
Data Management: Session state and conversation history are stored in DynamoDB for quick access, while structured data is stored in RDS
Security: IAM policies ensure each component has only necessary permissions
This architecture provides scalability, security, and flexibility for building intelligent multi-agent systems powered by LLMs and RAG pipelines.