Turn your GenAI prototype into an automated pipeline using Apache Airflow

Build, debug, and optimize AI agents using DSPy and MLflow.

Build AI apps that access tools, data, and prompts using the Model Context Protocol.

Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.

Build agents that navigate and interact with websites, and learn how to make them more reliable.

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Build agents that collaborate to solve complex business tasks.

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Learn a flexible framework to build a variety of complex AI applications.

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.