Hands-On Project: Build Agentic AI with AWS Bedrock AgentCore

👉 Get a $120K–$250K+ Job in AWS Cloud, AI, Gen AI & Agentic AI. Check out the next step: https://go.k21academy.com/3TRrmx8 ✅Want your personalised Al career roadmap? Answer a few quick questions (less than 2 minutes) and let our Senior Al Career Strategists create one for you: https://visit.k21academy.com/f89 ✅ FREE LIVE MASTERCLASS: How to Land a $300K+ Job in AI/ML, GenAI & Agentic AI in Less Than 6 Months: https://visit.k21academy.com/vqn _________ Most AI professionals can explain what an AI agent does. Very few can walk an interviewer through one they've actually built. That gap is what costs people the role. In this video, you'll see a full production AI agent built end to end on AWS Bedrock — memory, authentication, tool use, observability, and a real user interface — broken into five labs you can follow one at a time. If you're a Cloud Architect, Solutions Architect, DevOps Engineer, ML Engineer, or AI Engineer — or you're working toward any of these roles — this is the kind of project that changes how you show up in an interview. Watch it through. Then come back to the description for the next step. What you'll cover: Full AgentCore architecture: Runtime, Memory, Gateway, Identity Authentication and authorization using Amazon Cognito Tool use via AWS Lambda for web search and warranty lookups Short-term and long-term memory for session continuity CloudWatch observability and monitoring End-to-end Streamlit frontend for real user interaction How to explain this project in a job interview — the business problem, the numbers, and what the agent actually solves Prerequisites: AWS account, SageMaker notebook setup, basic Python familiarity (non-developers can follow the architect/DevOps track). --- TIMESTAMPS 0:00 Introduction and what you'll build 0:56 Full AWS architecture walkthrough (Bedrock, SageMaker, AgentCore) 2:03 AgentCore components: Observability, Identity, Gateway 3:04 Memory: short-term and long-term 3:48 The business problem this agent solves 4:25 Agent performance numbers: cost, speed, resolution rate 5:14 How AI agent technology has evolved (2010 to today) 6:21 What makes a real AI agent: reasoning, tools, memory, auth 7:36 LLMs overview: Claude, AWS, OpenAI and choosing your model 8:08 Tools, RAG, and external integrations 9:03 Authentication and authorization in agentic systems 9:28 Prerequisites: who this is for (architects, DevOps, developers) 10:55 AWS account setup and warming up Bedrock and SageMaker 12:49 End-to-end flow: how the user query moves through the system 13:32 The 5-lab project structure 14:00 Lab 1: Build the basic agent prototype (no memory) 14:23 Lab 2: Add memory (short-term and long-term) 14:30 Lab 3: Scale with gateway, identity, and AWS Lambda 14:54 Lab 4: Production deployment on AgentCore Runtime + CloudWatch 15:25 Lab 5: Build the Streamlit frontend (customer-facing UI) 16:03 Final end state: complete production customer support agent 17:12 How to access the full project inside the K21 program 17:24 The 3-step job framework: from not confident to multiple offers 19:04 Free masterclass walkthrough and how to register --- 46,000+ IT professionals trained | 91% placement rate 4.8 Google Rating | 4.6 Trustpilot ✅Want your personalised Al career roadmap? Answer a few quick questions (less than 2 minutes) and let our Senior Al Career Strategists create one for you: https://visit.k21academy.com/f89 ✅ FREE LIVE MASTERCLASS: How to Land a $300K+ Job in AI/ML, GenAI & Agentic AI in Less Than 6 Months: https://visit.k21academy.com/vqn #AWSBedrock #AgentCore #AgenticAI #AWSCloud #AIAgent #K21Academy