[AI]AWS’s legacy will be in AI success

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As the company that kick-started the cloud computing revolution,

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is one of the world’s biggest companies whose practices in all things technological can be regarded as a blueprint for implementing new technology.

This article looks at some of the ways that the company is deploying AI in its operations.

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’s latest AI strategy has progressed from basic chatbots to

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: systems that can plan and execute multi-step work using different tools and across processes. As a company,

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sits at the intersection of cloud infrastructure (in the form of AWS), logistics, retail, and customer service, all of which are

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can have massive impact.

Copilots to agents, AWS builds the control plane autonomy

In early 2025,

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made its AI intentions clear for its cloud company, AWS, by forming a new group focused internally on agentic AI. According to reporting on an internal email, AWS leadership

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as a potential “multi-billion” business, underscoring that the technology is regarded as a new platform layer, not a standalone feature.

The company was not afraid to say that its workforce is

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because of the technology. In June 2025,

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CEO Andy Jassy told employees that widespread use of generative AI and agents will change how work is done, and that over the next few years,

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expects routine work to become faster and more automated, slowing hiring, changing roles, and shrinking some job categories, even if other categories grow.

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’s best use cases are high-volume, rules-bound workflows that require a lot of searching, checking, routing, and logging. These are or will have significant impact in forecasting, delivery mapping, customer service, and product content. /

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examples like inventory optimisation, improved customer service, and better product detail pages as internal targets for gen AI.

Logistics and operations

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has described AI-enabled upgrades in its US operations that hint at where an agentic approach may take shape. In June 2025, it outlined AI innovations that included a generative AI system to improve delivery location accuracy, a new demand forecasting model to predict what customers want (and where), and an agentic AI team looking at

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to understand natural-language

Consumer-facing agents

Consumer agents are where autonomy first becomes real, because systems can take actions, even where there’s money involved. Reporting in

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highlighted features like monitoring items for price drops and (optionally) purchasing for the user automatically once a threshold is hit, a concrete example of the agentic concept in everyday terms: users setting constraints (in the form of price thresholds), and the

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inside said boundaries.

Rufus as the

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AI interface

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’s Rufus assistant is positioned as an AI interface to shopping, one that helps customers find products, do comparisons, and understand the trade-offs between various choices.

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as powered by generative (

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) AI to make shopping faster, with personalisation created by a user’s shopping history and current context. Agents therefore become the a shopping interface, with their value to the retailer in shortening journey from intent to final purchase.

Agents for

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Bedrock and AgentCore

Internally, AWS is producing agentic ‘building blocks’. Agents for

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are designed to execute multi-step tasks by orchestrating models with tools use and integration with other platforms. The

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Bedrock AgentCore is presented as a platform to

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[PDF], deploy, and operate agents securely at scale. It has features like runtime hosting, memory, observability dashboards, and evaluation.

AgentCore is

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’s attempt to become the default infrastructure layer for supervised enterprise agents, especially for organisations that need auditability, access controls, and reliability.

Keeping an eye on workforce and governance

If

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succeeds, the next phase for the technology is managed AI, comprising of mechanisms that grant or revoke permissions for tools and data access, the monitoring of agents’ behaviour, evaluation of performance and whether governance guidelines are being met, and the establishment of escalation paths when agents hit uncertainty.

The signals to the workforce have been baked into leadership messaging at the company. Fewer people will be required for some corporate tasks, and there will be more roles that can design workflows, govern the models, keep systems secure, and audit the outcomes of agentic AI use.

Conclusions

Proven as a leader in technology,

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’s stance on AI and the meaningful ways in which it’s implementing AI are a description of the paths enterprise companies may follow. Winning the productivity gains and lowered costs that AI technology promises is not as simple as plugging in a local device, or spinning up a new cloud instance. But the company can be seen as lighting the way for others to follow. Whether it’s supervising agents or deflecting customer queries to automated answering systems, AI is changing this technology giant in every possible way.

(Image source: CHEN – The Arousing, Thunder – arouse, excite, inspire; thunder rising from below; awe, alarm, trembling; fertilizing intrusion. The ideogram: excitement and rain” – public domain)

 

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