Generative AI Apps and Agents on Amazon Bedrock is a hands-on developer course in building generative AI applications on AWS with Python, from the Converse API, RAG with Bedrock Knowledge Bases and Guardrails to AI agents on AgentCore. It suits developers and cloud engineers taking AI into production, and you leave with a working internal AI assistant prototype.
Building a generative AI chatbot prototype takes a few hours, but taking the same application into production in an organisation is another matter. Development teams have to decide which model to use, what each request costs, whether answers cite the right documents, how to stop personal data leaks and prompt injection, and, once AI agents can call other systems on their own, how to control their permissions and trace what they do. Amazon Bedrock offers services for all of this, but you need to know how to fit the pieces together.
This course takes developers through building generative AI applications on AWS with Python from start to finish. It begins with calling models through the Converse API, streaming, tool use and cost control, moves on to RAG with Amazon Bedrock Knowledge Bases, rerankers and quality measurement, protects the application with Amazon Bedrock Guardrails, IAM and logging, and then builds AI agents with Bedrock Agents and Strands Agents before deploying them to Amazon Bedrock AgentCore with Memory, Gateway, Identity and Observability. It closes with a capstone internal AI assistant. The content helps build skills aligned with the AWS Certified Generative AI Developer - Professional (AIP-C01) exam. (3 days, 6 hours per day, 18 hours in total, Intermediate level.)
What you’ll gain
Call foundation models on Amazon Bedrock with the Converse API, including streaming, multimodal input and tool use
Choose models and design calls cost-effectively with prompt caching, batch inference and cross-Region inference
Version prompts and test their quality before releasing them
Build RAG with Bedrock Knowledge Bases and improve it with chunking, metadata and rerankers
Guard against harmful content, personal data exposure and hallucination with Bedrock Guardrails
Build AI agents with Bedrock Agents and Strands Agents and connect tools through MCP
Deploy agents to AgentCore Runtime with Memory, Gateway, Identity and Policy
Monitor, evaluate and control the cost of generative AI applications in production
Who this course is for
Backend and full-stack developers building generative AI applications on AWS
Cloud engineers and solutions architects designing RAG systems or AI agents for their organisation
AI and innovation teams that need to take prototypes into production safely
Anyone building skills aligned with the AWS Certified Generative AI Developer - Professional exam
People who have passed AWS AI Practitioner and want to build real applications
Prerequisites
Python programming, including functions, dictionaries, JSON and installing packages
Basic use of the AWS console and a working knowledge of IAM, S3 and Lambda
A basic grasp of LLMs and prompts; having passed AWS AI Practitioner makes the course smoother
A laptop that can run Python 3, VS Code and the AWS CLI, plus an AWS account on a Paid Plan
Curriculum
Course Details
This is a developer course in building generative AI applications and AI agents on Amazon Bedrock. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with Python coding labs every day. Intermediate level. Every lab runs in the learner's own AWS account or a company sandbox on a Paid Plan, with usage charges on that account; the course uses small models, S3 Vectors by default and clean-up
steps after each lab. It builds on the RAG & Knowledge Base course and focuses on AWS managed services from RAG through to agents in production. The content is aligned with some of the skills in the AWS Certified Generative AI Developer - Professional (AIP-C01) exam, but it is not official AWS exam preparation and does not include the exam fee. Learners take home a lab guide, code for every lab and a capstone internal AI assistant project.
Day 1Calling Foundation Models Professionally
Section 1: Lab: Preparing Your AWS Account and Amazon Bedrock
Generative AI services on AWS: how Bedrock, AgentCore and SageMaker AI differ
Choose a Region, check model access and understand cross-Region inference profiles
Create least-privilege IAM roles and policies for developers and applications
Set up the AWS CLI, boto3 and local credentials safely
Lab: set AWS Budgets and call a model from Python for the first time
Section 2: Lab: Calling Models with the Converse API
Converse API structure: system prompt, messages and inference configuration
Send images and documents to a model as multimodal input
Stream responses with ConverseStream for a smoother user experience
Handle throttling and errors with exponential backoff retries
Lab: build a summary API that accepts both text and PDF files
Section 3: Choosing Models and Controlling Cost
Compare Bedrock model families on quality, speed, language and price
Calculate cost per request from input and output tokens
Cut costs with prompt caching, batch inference and smaller models
On-demand versus provisioned throughput, and designing model routing
Lab: measure quality, latency and cost of three models on the same task
Section 4: Lab: Prompt Engineering and Prompt Management
Write system prompts and few-shot examples and enforce JSON output
Store prompts as templates with variables and versions in Bedrock Prompt Management
Chain several steps into a workflow with Amazon Bedrock Flows
Run prompt regression tests before changing a version in production
Lab: version the prompts of a customer request classification task
Section 5: Lab: Tool Use and Function Calling
Define tools with JSON Schema and let the model decide when to call them
Write the loop that receives tool requests, runs functions and returns results
Validate parameters and handle tool errors so the app stays up
Use AWS Lambda as a tool that connects to internal systems
Lab: an assistant that answers order status questions from a mock system
Day 2RAG, Guardrails and Security
Section 6: Lab: Embeddings and Vector Stores on AWS
How embeddings work, and choosing an embedding model and dimension
Vector store options: S3 Vectors, OpenSearch Serverless and Aurora PostgreSQL with pgvector
Compare minimum cost, speed and capabilities of each option
Similarity search, metadata filters and hybrid search
Lab: embed Thai documents and run similarity search in Python
Section 7: Lab: RAG with Amazon Bedrock Knowledge Bases
Create a knowledge base from Amazon S3 and choose a vector store
Fixed-size, hierarchical and semantic chunking and how to choose
Parse documents with tables and images using a foundation model or Bedrock Data Automation
Retrieve versus RetrieveAndGenerate, with citations in the answer
Lab: a chatbot that answers from the company handbook with citations
Section 8: Improving and Measuring RAG Quality
Add metadata to documents to filter by department, date or permission
Improve accuracy with reranker models and query reformulation
Sync new content incrementally when source documents change
Measure RAG quality with Amazon Bedrock Evaluations and LLM-as-a-judge
Lab: compare results before and after tuning chunking and reranking with a test question set
Section 9: Lab: Amazon Bedrock Guardrails
Content filters, denied topics and word filters for business applications
Sensitive information filters to mask or block personal data
Contextual grounding checks and Automated Reasoning checks to reduce hallucination
Use the ApplyGuardrail API with any model and defend against prompt injection
Lab: add a guardrail to the RAG chatbot and test it with attack prompts
Section 10: Security and Governance for Generative AI Applications
Least-privilege IAM for models, knowledge bases and agents
Encryption with AWS KMS and private connectivity through VPC endpoints
Model invocation logging, CloudTrail and keeping logs without exposing personal data
Using company data with models in line with PDPA and internal policy
Workshop: a security checklist before opening a generative AI app to real users
Day 3AI Agents from Prototype to Production
Section 11: Lab: AI Agent Concepts and Bedrock Agents
How agents differ from chatbots: reasoning, tools, memory and planning
ReAct, fixed-step workflows and multi-agent patterns
Managed Bedrock Agents with action groups and knowledge bases
Choosing between managed agents and building your own with a framework
Lab: build a Bedrock agent that searches the handbook and opens tickets through Lambda
Section 12: Lab: Building Agents with Strands Agents
Strands Agents SDK structure: model, system prompt and tools
Create tools with a Python decorator and use tools from an MCP server
Multi-agent patterns: agents as tools, graph and swarm
Set stop conditions, timeouts and permission boundaries for agents
Lab: an agent that analyses sales data by calling several tools
Section 13: Lab: Deploying with Amazon Bedrock AgentCore
AgentCore overview: Runtime, Memory, Gateway, Identity, Policy and Observability
Deploy an agent to AgentCore Runtime with the AgentCore CLI
Add short-term and long-term memory so the agent remembers user context
Turn Lambda functions and internal APIs into MCP tools through AgentCore Gateway
Authenticate and restrict tool calls with AgentCore Identity and Policy
Section 14: Observability, Evaluation and Operations
Trace each step of an agent with AgentCore Observability and CloudWatch
Measure agent quality with AgentCore Evaluations and the team's own test sets
Set alarms for errors, latency and unusual token usage
CI/CD and rollback approaches when changing models or prompts
Lab: find why an agent answered wrongly from its trace and fix it
Section 15: Capstone: An Internal AI Assistant
Design an architecture that combines RAG, guardrails and an agent
Build an assistant that answers from documents and acts through tools
Evaluate quality, safety and cost per use
Present the work and map the skills used to the AIP-C01 exam domains
Delete all resources and wrap up with a pre-production checklist
Schedule & training options
For individuals — public rounds
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
Who is Generative AI Apps and Agents on Amazon Bedrock for, and what background is needed?
Built for Backend and full-stack developers building generative AI applications on AWS · Cloud engineers and solutions architects designing RAG systems or AI agents for their organisation · AI and innovation teams that need to take prototypes into production safely Background you should have: Python programming, including functions, dictionaries, JSON and installing packages · Basic use of the AWS console and a working knowledge of IAM, S3 and Lambda Not sure the fit is right? Talk to our team on LINE @itgenius or call 02-570-8449.
How much does Generative AI Apps and Agents on Amazon Bedrock cost and how long does it run?
THB 9,900 (currently THB 8,910 on promotion). The course runs 18 hours. The price excludes 7% VAT (for payment in a company's name). Pay by bank transfer to the company account, confirm it on our payment page, and we can issue the receipt or tax invoice in your company's name.
Do I get a certificate?
Yes. Everyone who completes the course receives a Certificate of Completion from IT Genius Institute. Each certificate carries its own number, and anyone holding that number can verify it online on our certificate page, so you can add it to your portfolio or pass it to HR as evidence of training.
Where does the training take place, and is there an online option?
You can attend onsite at IT Genius Institute or arrange to join online, and we also run it as a private in-house session for your team. Ask about dates and venues on LINE @itgenius or call 02-570-8449.
What if I fall behind or miss a session — can I retake it?
Yes. You may retake the same course free of charge in a later round, under the institute's conditions. Tell our team which course and round you attended, and we will check it and offer you the rounds that still have seats. Ask us on LINE @itgenius or call 02-570-8449.
How do I enrol, or request a quotation for my company?
Enrol online with the registration form on this page. You can register several attendees at once and enter your tax ID and billing address for the tax invoice. Or request a company quotation straight from the quote button. For anything else call 02-570-8449 or reach us on LINE @itgenius.
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Generative AI Apps and Agents on Amazon Bedrock18 hrs · 3 days
