If you use AI agents, you’ve probably explained the same task more than once. You might describe how to build your weekly status update, write release notes, manage a project, or create tickets. These are all repeatable tasks that you can encode using AWS Quick through agents and skills. AWS Quick is an AI-powered service for automating tasks, analyzing data, building web applications, and conducting research. You work with it in natural language, and Quick uses AI agents to process your requests against your connected data sources and applications.
In this post, we cover how agents, skills, and your data fit together in AWS Quick, and how to build and share skills that work for your team. We also show how the official catalog and custom agents help you put those skills to use.
In this section, we explain the foundational concepts that help you understand AWS Quick in relation to agents, skills, and your data.
Figure 1: How a Quick agent follows its instructions, loads the skills a task needs, acts through connectors, and grounds its answers in your data
The agent sits at the center of Figure 1. It follows its instructions, loads the skills that a task calls for, takes action through connectors, and grounds its answers in your data: spaces, knowledge bases, datasets, and dashboards.
An agent in AWS Quick is a configurable AI with its own instructions, knowledge sources (spaces), and connectors, which it uses to answer questions and get work done for you. You can work with an agent in chat, or let it run on its own using a routine .
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