Purpose-Built AI Agents

Most GenAI applications today are reactive, built for single-step tasks. On the AWS platform, you can move beyond this to create agentic AI—autonomous systems that can reason and perform complex, multi-step workflows.

• Reactive vs. Agentic AI: Reactive AI responds to a single command (e.g., "summarize this document"). Agentic AI can complete a full goal (e.g., "research a topic, draft a summary, and email it to the team").

• The Role of Amazon Bedrock: Bedrock serves as the central hub for agentic AI on AWS. It provides a unified API to access leading foundation models, including the new Amazon Nova family, allowing you to choose the best model for your specific needs.

• Data Foundation (RAG & Vector Databases): True agentic solutions are powered by your data. Retrieval-Augmented Generation (RAG) links your model to a private vector database, enabling the agent to retrieve relevant information from your data at runtime, ensuring highly accurate and context-aware responses.

• AI Protocols & Frameworks: AI protocols and open-source frameworks like AWS Strands Agents provide the scaffolding for your agent. They give the agent a structured way to plan, interact with other tools and APIs, and work collaboratively with other specialized agents to achieve complex goals.

• Prompt Engineering: Advanced prompt engineering is a key technique for guiding your agent's behavior and performance, ensuring it stays on task and adheres to your business logic.