Amazon is adding the Activity Feed and other features from its Quick desktop app to the AI assistant’s mobile apps for iOS and Android.

The Activity Feed is the signature feature of Amazon Quick. It combines email, Slack messages, calendar invites and CRM updates into one prioritized list, and lets people act on items (opening and responding to emails, for example) without switching apps.

Amazon said Wednesday that the Quick desktop app, released in preview in April, is now generally available on Windows and macOS. The company also said Quick’s agents now run in the cloud, so they keep working after a laptop is closed and deliver results to the feed.

The desktop and mobile apps now sync, as well, so a task started on a laptop can be picked up on a phone, for example.

Quick has a free tier, with paid individual plans starting at $20 per user per month billed annually, and business plans running $20 to $40 per user per month.

Quick is Amazon’s entry in a crowded market for AI assistants at work, competing with Microsoft Copilot, Google Gemini, OpenAI, Anthropic and others. Amazon’s announcements cited business customers for Quick including Southwest Airlines, LabCorp and the PGA Tour.

The desktop app came together fast, as part of a new effort inside Amazon to use small teams to move quickly: Swami Sivasubramanian, the AWS vice president of agentic AI, told GeekWire in June that a team of about six engineers started in late January and shipped April 28.

Amazon just broadened the role of Swami Sivasubramanian, the VP behind AWS’s agentic AI push, expanding and renaming his organization to include emerging technologies, and giving him a larger mandate to shape AI strategy and technical direction across the cloud division.

Sivasubramanian announced the change in a LinkedIn post on Wednesday, saying he will now lead the “Agentic AI & Emerging Technologies” organization, with an expanded title to match.

He described emerging technologies as “the work that doesn’t fit neatly into a team because it doesn’t exist yet.” It’s a type of work he’s done before, including formative roles with DynamoDB, now one of AWS’s most widely used databases, and Bedrock, the platform through which AWS customers access AI models from Anthropic, Meta, and others.

“When the industry is changing this quickly, it’s important to step back, pressure-test ideas, and see the big picture to help teams scale their impact,” he wrote.

Sivasubramanian’s agentic AI division has operated as a test case for running Amazon like a startup, with small teams shipping products in months that once took a year, as GeekWire reported in June.

He’ll continue overseeing the teams behind Kiro, Amazon Quick, and AWS Transform, while taking on new areas including neurosymbolic AI and a recently announced service called AWS Context, which builds a knowledge graph from a company’s existing data so AI agents can query it.

The expansion builds on moves already underway, including the hiring in May of former Microsoft security exec Shawn Bice to lead AWS’s Automated Reasoning Group, which uses mathematical techniques to verify that AI agents are doing what they’re supposed to do.

It also coincides with a broader reshuffling of Amazon’s AI strategy and teams.

Amazon laid off employees in its artificial general intelligence organization last week and confirmed the closure of its San Francisco AGI site, but noted that its frontier model research will continue under Pieter Abbeel, who joined Amazon through its acquisition of robotics startup Covariant.

Business Insider reported Monday that Amazon is winding down most of its in-house Nova foundation models, including its high-end Premier and Omni models, and concentrating engineering talent and computing resources on a smaller number of frontier efforts.

Sivasubramanian’s expanded role is separate from the AGI changes, and the two organizations operate independently of one another within Amazon. An Amazon spokesperson said the company’s commitment to building frontier models “is as strong as it’s ever been,” while noting that it continually evolves its model lineup based on customer needs.

The broader reshuffling mirrors a larger pattern across the industry, as big AI providers look to ensure the billions they’re sinking into chips and data centers pay off in customer outcomes and business growth.