Ship it! Building Production Ready Agents — Mike Chambers, AWS

Generative AI and Agent Technology - Amazon Web Services (AWS) specializes in generative AI, evolving from machine learning [1] - The presentation focuses on deploying generative AI agents to cloud scale, targeting both developers and leaders [1] - The core components of an agent include a model for natural language understanding, a prompt defining the agent's role, an agentic loop for processing input and using tools, history for maintaining context, and tools for external interaction [1][2] - AWS Bedrock offers a suite of capabilities for building generative AI components, including models from Anthropic, Meta, and Mistral [2] - Amazon Bedrock Agents is a fully managed service for deploying agents without infrastructure management [2] Practical Implementation and Tools - The demonstration uses a simple Python agent with a dice rolling tool, initially running locally on a laptop with the Llama 3 8 billion parameter model [1] - The agent is configured with instructions (similar to a prompt) and action groups, which connect to tools [2] - Lambda functions are used to host the tools, enabling them to perform various actions, including interacting with other AWS services [2] - The AWS console provides a user interface for creating and configuring agents, including defining parameters and descriptions for tools [3][4][5][6][7][8][9][10][11][12][13][14][15] - Amazon Q developer is integrated into the console's code editor, offering code suggestions [17][18][19][20][21] Deployment and Scalability - The presentation emphasizes deploying agents to a production-ready, cloud-scale environment [1] - Infrastructure as code frameworks like Terraform, Palumi, and CloudFormation can be used for deployment [3] - AWS offers free courses on deeplearning.ai with AWS environments for experimenting with Amazon Bedrock Agents [25]