Introduction to Agentic AI Systems on AWS
Learn what makes AI systems agentic and how to design autonomous AI architectures on AWS using Amazon Bedrock, Lambda, Step Functions, and persistent memory services.
Welcome to Introduction to Agentic AI Systems on AWS, your foundation for understanding and building autonomous AI systems that perceive, reason, act, and learn.
As organizations move beyond traditional chatbots and static automation, agentic AI represents a fundamental shift in how AI systems operate. Rather than responding to individual prompts, agentic systems pursue goals autonomously, execute complex workflows, and adapt based on outcomes.
What will you learn?
- Define what makes AI systems agentic and how they differ from traditional language models and chatbots.
- Understand the four-stage operational cycle (perceive, reason, act, learn) and the five core components (perception, memory, planning, tool calling, communication) that enable autonomous operation.
- Identify which AWS services map to each agentic component, including Amazon Bedrock for orchestration, Lambda for tool execution, Step Functions for workflows, and DynamoDB and S3 for memory.
- Recognize key architectural decisions when designing agentic systems, including managed versus custom orchestration, knowledge base integration, and deployment patterns.
- Explore real-world use cases where agentic systems deliver business value, from automated support agents to DevOps automation and cost optimization.
Intended Audience
This lesson is recommended for cloud practitioners, AI and ML engineers, and technical decision-makers who want to understand agentic AI concepts and evaluate how autonomous systems can solve business problems using AWS.
Feedback
Here at QA, we strive to keep our content up to date in order to provide the best training available. If you have any feedback, positive or negative, please contact platformsupport@qa.com. Thank you!