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Today, Quick gets even more powerful: new autonomous agents that work continuously on your behalf, an activity feed that helps you prioritize your most important work, and the ability to find insights across every data source your business runs on from a single question.
What if you came back from a full day of meetings and the busywork was already done? Stalled deals followed up on. Compliance changes summarized. Meeting prep written. Not because you multi-tasked, but because something was working in the background while you focused on other urgent priorities.
Teams are already using Amazon Quick — an AI assistant that connects to your most-used apps and data sources, learns how you work, and acts on your behalf — to reclaim hours every week and focus on the work that matters. Today, Quick gets even more powerful: new autonomous agents that work continuously on your behalf, an activity feed that helps you prioritize your most important work, and the ability to find insights across every data source your business runs on from a single question.
Starting today, you can build autonomous agents in Quick that handle tasks on your behalf, continuously, even while you’re in back-to-back meetings. Create one in minutes by describing what you need in plain language or choose from a library of pre-configured agents. You decide how much autonomy to give each agent, from precise step-by-step instructions to broad goals where agents figure out the path on their own. Each agent operates within the guardrails you set.
Back-to-back in meetings all day? You return to find stalled deals flagged, follow-ups drafted, and customer relationship management (CRM) notes updated. Responsible for navigating complex regulations? A change drops overnight and an impact summary is in your inbox by morning. Buried in purchase orders? Your agent handles them around the clock so your team can focus on strategic negotiations. Whatever your role, you can build agents that work continuously on your behalf, with no coding required.
You can monitor progress, provide additional inputs, and review agent outputs directly in Quick without losing context. Every interaction, correction, and outcome makes your agents better over time, like colleagues who get sharper every week.
In the following example, we create an agent that monitors legislative changes that impact packaging requirements for manufacturers: