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

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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).

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Free Resources

Join the Free Masterclass — How to Land a $300K+ Job in AI, Data or Cloud:
https://visit.k21academy.com/usu

Agentic AI Interview Q&A Guide: https://visit.k21academy.com/b7p

AWS AI/ML Interview Questions Guide: https://visit.k21academy.com/175a72
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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

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Видео Hands-On Project: Build Agentic AI with AWS Bedrock AgentCore канала Atul @ K21Academy