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
