Agentic AI Engineering on AWS

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:

• Software engineers, backend developers, and DevOps/platform engineers who want to build production LLM-powered applications
• ML engineers and data scientists moving from experimentation to production agentic AI systems
• Technical leads and architects evaluating how to structure AI platforms for their organizations

Prerequisites:

• Intermediate Python proficiency
• Basic familiarity with AWS (an AWS account is needed for the labs)
• Comfort with the command line and containers (Docker basics)
• Basic familiarity with Kubernetes and Terraform is helpful for the deployment sections, but not strictly required
• No prior AI/ML experience required. We cover the fundamentals before going deep

By the end of this course, you will have the skills and confidence to architect, deploy, and operate production agentic AI systems in real enterprise environments. Stop building demos. Start building production AI platforms.