Agentic workflows handle unpredictable tasks based on user input, like making API calls. A serverless architecture efficiently manages these tasks and varying workloads without maintaining servers, enabling faster deployment.

You will learn to protect sensitive information and shield customers from harmful content by employing agents with guardrails. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails. The serverless setup is ideal for agents that might need to access many tools or APIs on demand. You’ll explore this through hands-on examples where you’ll: 1. Build a customer service bot for a fictional tea mug business that can handle tasks like answering queries, retrieving information, and processing orders. 2. Connect multiple types of agent actions, and implement guardrails for responsible operation. 3. Use Amazon Bedrock’s fully managed services to deploy and scale the bot efficiently. The course will implement two elements essential to the deployment of business applications: 1. Serverless deployment to achieve rapid scaling and seamless operation without the need to manage infrastructure. 2. Responsible agent to protect your application from malicious prompts and unintended outputs by configuring guardrails. In detail, here’s what you’ll do: 1. Use Amazon Bedrock to create an AI agent, explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. 2. Connect your customer service agent to services like a CRM to get customer details and log support tickets in real time. 3. Attach a code interpreter to your agent, giving it the ability to perform accurate calculations, where it writes and runs its own Python code to support its response. 4. Implement and configure guardrails to prevent your agent from revealing sensitive information and using inappropriate language. 5. Connect your agent to a repository of customer support documents that discuss many issues that the agent can resolve directly or choose to escalate, if necessary, to a human workflow. 6. Get a walkthrough of the Amazon Bedrock interface in the AWS console to configure agents, set the guardrails, and connect to knowledge databases, all in an easy-to-configure graphical interface. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios, fully serverless, and ready to scale.