Mike Chambers, a Developer Advocate for AWS, specializes in generative AI and focuses on getting agent code into production at cloud scale.
He previously contributed to the "Fundamentals of LLMs" course, which has been taken by over 370,000 people.
A simple Python agent, demonstrated running locally on a laptop, uses a Llama 3.1 8 billion parameter model.
This agent incorporates a "dice roller" tool and uses a system prompt with examples to interpret natural language commands.
In a demonstration, the agent successfully processed a command to "roll for initiative and add a dexterity modifier of five," using its tool to roll a D20 and add the modifier, yielding a result (e.g., 15).
The presentation aims to show how to transition this low-scale local agent code to a production-ready, cloud-scale environment.
For an agent to operate at cloud scale, several core components are essential:
Model: Provides natural language understanding, with pre-existing models making this component relatively straightforward to integrate.
Prompt: Defines the agent's purpose, capabilities, and personality.
Loop (Agentic Loop): Enables the agent to "think" by processing input, using tools, evaluating outcomes, and deciding if further actions or loops are necessary.
History (Conversational History): Crucial for the agent to remember its internal reasoning steps and tool calls within the context of a conversation, ensuring continuity.
Tools: Provide the agent with the ability to perform actions and interact with the external world.
These elements are considered the fundamental requirements for a minimal viable agent product.
AWS offers services designed to host and scale these agent components in the cloud.
Models: Amazon Bedrock provides access to models from various leading providers, including Anthropic, Amazon Nova, Meta, Mistral, and AI21 Labs.
Amazon Bedrock Agents: This is a fully managed service that eliminates the need for users to manage infrastructure, ensuring agents are cloud-scale.
Within Bedrock Agents, the agent's "instruction" (personality/prompt) is configured, while the service automatically handles the agentic loop and conversational history.
Action Groups: These are collections of tools that connect the agent to specific functionalities, typically implemented using AWS Lambda functions.
Lambda functions are well-suited for hosting these tools due to their inherent scalability and ability to interact with external services or other AWS services.
A demonstration showcased building an agent directly in the AWS console, noting that all steps are also achievable via Infrastructure as Code frameworks (e.g., Terraform, CloudFormation, SDK, SAM).
The process involved creating and naming the agent, providing a descriptive purpose, selecting a model (e.g., Anthropic Haiku 3.5), and defining its instructions (e.g., "You're a games master...").
An action group was then added, linked to a Lambda function (conveniently set up using a quick start option) which housed the "roll dice" tool's logic.
Parameters for the tool, such as numberOfSides (specified as a required integer), were defined and described for the LLM's comprehension.
Python code for the dice roll, including import random, was inserted and deployed within the Lambda function's console editor, with assistance from Amazon Q Developer.
After preparing the agent (which includes setting up alias IDs for a production-ready software development lifecycle), it was tested in the console.
The agent successfully responded to a natural language query, confirming its fully hosted and managed cloud-scale functionality.
Free DeepLearning.AI courses focused on Bedrock agents are available, offering a complimentary AWS environment for hands-on experimentation.
The speaker extended an invitation for further discussions on topics such as cloud-scale MCP servers, an open-source SDK for model-first agents, and acquiring real-life D20 dice.