Presenters from AWS introduce the topic: building AI agents with Claude models via Amazon Bedrock.
Amazon Bedrock is described as a fully managed service that gives access to foundational AI models through a unified API, providing scalability, model choice, guardrails, and enterprise-grade security.
"Agents" are defined as autonomous systems that can reason, plan, and perform multi-step tasks to achieve objectives, similar to human workflows.
AWS has released an open-source SDK called "Strands Agent" for building agentic AI applications.
Strands Agent SDK focuses on simplicity: it requires only models, tools, and prompts to create an agent.
The SDK lets the large language models (LLMs) handle more reasoning and actions, providing model flexibility and reducing the need for extensive code scaffolding.
Strands comes with built-in tools and supports easy deployment across AWS services like EC2, Lambda, and ECS.
The SDK, documentation, and GitHub links are shared, and community contributions are encouraged.
A video example demonstrates using Strands Agent to generate a math animation video (similar in style to "3Blue1Brown") by scripting a quadratic equation plot, powered by Claude 3.7 and Strands.
All code and workshop materials are open source and available for replication.
First Exercise: Weather and Word Count Agent 10:44
The initial workshop exercise involves building an agent that retrieves weather data and counts words in the response using Strands.
Demonstrates importing tools, creating system prompts, and leveraging the HTTP request tool (which is built-in and requires no API keys).
Models can be swapped easily (e.g., Claude 3.5), and custom tools can be created with simple decorators.
Querying "What's the weather like in San Francisco?" shows the agent fetching data and performing word count in about 44 lines of code.
Strands agent is highlighted as easy to use compared to other agentic frameworks due to minimal boilerplate and simple tool creation.
The model autonomously determines how to extract geographical data (e.g., latitude, longitude) from queries and interact with APIs.
Second Exercise: Using MCP Servers for Documentation and Diagrams 15:29
Introduces MCP (Model Connection Protocol) servers, which facilitate complex tasks like searching documentation or generating AWS diagrams from prompts.
Example demonstrated: an agent queries AWS Lambda documentation and creates a static website architecture diagram, interacting with multiple MCP servers in about 40 lines of code.
The workflow breaks tasks into steps, using HTTP calls to search, read, and visualize, with results varying based on context.
MCP servers can be hosted behind API Gateway and Lambda functions for server-side event streaming; open-source code samples are available for cloud deployment.
Third Exercise: Building a CDK Agent with Claude Code 20:30
Demonstration of creating an agent for AWS CDK (Cloud Development Kit) tasks via Claude-powered code assistance.
Claude Code assists in creating a new Strands agent that connects to an MCP server by referencing prior files and generating the needed code.
The agent is run, and it generates step-by-step CDK code examples (e.g., to create an S3 bucket), showing the agent's ability to guide and produce code automatically.
Claude Code reports on API cost, runtime, and changes made during the process.
MCP servers provide external context, allowing agents to look up documentation, perform cost analysis, draw AWS diagrams, connect to databases, and more.
They act as "USB-C" connectors for LLMs, enabling access to various resources such as documentation, cost tools, architecture diagram tools, and infrastructure as code integrations (CloudFormation, Terraform).
Multiple AWS-official MCP servers exist, catering to functions like cost analysis, front-end code, database interaction, and drawing.