As the company that started the cloud computing revolution, Amazon is one of the world’s largest companies, and its general technology practices can be seen as a blueprint for implementing new technologies.
This article highlights some of the ways the company is implementing AI into its operations.
Amazon’s modern AI strategy has progressed from basic chatbots to agent AI, systems that can use a variety of tools to plan and execute multi-step work across multiple processes. As a company, Amazon sits at the intersection of cloud infrastructure (in the form of AWS), logistics, retail, and customer service, all of which are areas where small efficiency gains can have a big impact.
In early 2025, Amazon established a new group within the company focused on agent AI to clarify its AI intentions for its cloud company, AWS. In an internal email report, AWS leaders said agent AI could become a “billion” business, emphasizing that the technology is considered a new platform layer rather than a standalone feature.
The company hasn’t been shy about saying publicly that it expects to lose its workforce because of the technology. In June 2025, Amazon CEO Andy Jassy told employees that the proliferation of generative AI and agents will change the way work is done, and over the next few years Amazon expects routine tasks to become faster and more automated, slowing hiring, changing roles, and shrinking some jobs even as others grow.
Amazon’s best use case is a high-volume, rule-bound workflow that requires a lot of searching, checking, routing, and logging. These have had or will have a significant impact on forecasting, delivery mapping, customer service, and product content. /Reuters/ pointed to examples of Gen AI’s internal goals such as optimizing inventory, improving customer service, and improving product detail pages.
Amazon has described AI-powered upgrades to its U.S. operations, suggesting an agent approach could take shape. In June 2025, we outlined AI innovations such as generative AI systems to improve delivery location accuracy, new demand forecasting models to predict what (and where) customers want, and agent AI teams looking to enable robots to understand natural language.
Consumer agents are where autonomy is first realized, as the system can take actions even when money is involved. Reporting The Verge For Alexa+, we highlighted things like the ability to monitor item price drops and (optionally) automatically make purchases for users when a threshold is reached. This is a concrete example of the concept of an agent in everyday parlance, where the user sets constraints (in the form of price thresholds) and the system monitors and executes within those boundaries.
Amazon’s Rufus Assistant is positioned as an AI interface to shopping that helps customers find products, compare them, and understand the tradeoffs between different options. Amazon says Rufus is powered by generative (and increasingly agentic) AI, which uses your shopping history and current context to create personalization and help you shop faster. The agent thus becomes a shopping interface, and its value to retailers lies in shortening the journey from purchase intent to final purchase.
Internally, AWS creates “building blocks” for agents. The Agent for Amazon Bedrock is designed to perform multi-step tasks by using tools and integrating with other platforms to tune your model. Amazon Bedrock AgentCore provides a platform for securely building (PDF), deploying, and operating agents at scale. Features include runtime hosting, memory, observability dashboard, and evaluation.
AgentCore is Amazon’s attempt to become the default infrastructure layer for monitored enterprise agents, especially for organizations that require auditability, access control, and reliability.
If Amazon is successful, the next stage of this technology will be managed AI. This consists of mechanisms for granting or revoking permissions for tools and data access, monitoring agent behavior, assessing whether performance and governance guidelines are being met, and establishing escalation paths when agents experience uncertainty.
Signals to employees are built into the company’s leadership messages. Fewer people will be needed for some enterprise tasks, and more roles will be needed to design workflows, manage models, ensure systems are secure, and audit the results of using agent AI.
As a proven technology leader, Amazon’s stance on AI and meaningful ways to implement it explains the path companies will likely take. Realizing the productivity gains and cost savings that AI technology promises is not as simple as connecting a local device or launching a new cloud instance. But the company can also be seen as lighting the way for others to follow. Whether it’s supervising agents or directing customer inquiries to automated response systems, AI is transforming the tech giant in every way.
(Image Source: CHEN – The Arousing, Thunder – Awakening, Excitement, Inspiration; Thunder rising from below; Awe, Alert, Trembling; Fertile Intrusion. Ideogram: Excitement and Rain” – Public Domain)
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