Agentic AI Engineering on AWS

Master AI Agent Development: Gateways, RAG, MCP, Multi-Agent Systems & AWS Infrastructure

What you’ll learn
✓ Design and build production-grade AI agents using real architecture patterns
✓ Build multi-agent workflows with evaluators, orchestration, and delegation
✓ Create LLM, Memory, and Retrieval Gateways for clean architecture
✓ Deploy AI agents to AWS using Terraform, EKS, and Kubernetes

Requirements
● Basic knowledge of Python programming
● Familiarity with AWS Services (IAM, EC2,EKS, or general cloud concepts)
● Comfort using the command line and Docker

Description
AI agents are everywhere. Production AI systems are not.

Most courses stop at prompts and demos. This course teaches you how to design, build, and deploy a production-grade agentic AI platform on AWS, the same way engineering teams build real systems at scale.

Across 31 hands-on lessons, you will build a complete multi-service platform from the ground up using Python, AWS Bedrock (Claude and Titan models), Terraform, Kubernetes (EKS), FastAPI, Docker, and Helm. This is not a toy project. It is a full production system with authentication, memory, retrieval, orchestration, observability, and secure service-to-service communication, all deployed on real AWS infrastructure.

What you will build and learn

• Agentic AI patterns: chaining, routing, parallelization, orchestrator-worker, and evaluator-optimizer workflows using LangGraph and Strands Agents

• Retrieval-Augmented Generation (RAG) with Bedrock Knowledge Bases, OpenSearch vector search, and a dedicated Retrieval Gateway

• Multi-agent systems with delegation, tool use, function calling, and Model Context Protocol (MCP)

• LLM Gateway architecture: model routing, abstraction, streaming, and cost control across Large Language Models

• Memory and state management with PostgreSQL (Aurora), Redis (ElastiCache), and persistent agent memory

• Observability and monitoring using OpenTelemetry, AWS X-Ray, and CloudWatch for full trace visibility across agents

• Infrastructure as Code: provision and deploy everything with Terraform and Kubernetes (EKS) using production Helm charts

• Prompt engineering fundamentals: chain-of-thought, few-shot examples, and structured evaluation techniques

What makes this course different

You will not just copy code. Every architectural decision is explained, why each service exists, what trade-offs were made, and how the components fit together. You will understand how to move from a single notebook prototype to a scalable, secure, enterprise-ready AI platform.

Who this course is for
■ Backend developers who want to build production AI agents
■ Cloud engineers exploring agent-based systems on AWS
■ DevOps engineers interested in AI infrastructure and orchestration
■ AI engineers moving from prototypes to production systems
■ Architects designing scalable multi-agent platforms
■ Anyone serious about building real-world AI systems, not demos

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