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When Atos set out to upskill 400 engineers from theory to delivery in agentic AI, the team faced a familiar challenge: how to build real-world capability, not only theoretical knowledge. Online courses and classroom-based instruction build foundations, but they do not always give teams the confidence or practical experience needed to apply AI effectively to […]

Generative AI spend behaves unlike any cost line before it. Traditional compute scales with provisioned capacity. AI spend scales with behavior: a single engineer running an agentic coding loop against a premium model can burn more tokens in a few hours than a team does in a week. This is the tokenomics problem: usage is

Amazon Quick proof of concept (POC) projects often succeed with a small pilot team, then stall when security and compliance teams review the production plan. A permission model that works for ten pilot users often breaks when you add five departments. Agents can return data outside their intended scope, and compliance teams struggle to audit

Built on our latest Nano Banana model, Google Pics — our image creation and editing tool — is now available. ​ 

An agentic system can research, reason, and orchestrate multi-step workflows, but the moment it hits a paywall, it stops. It has no wallet, no card, and no spending limit. t54 solved that problem. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. Their trust layer

Model Context Protocol (MCP) servers allow foundation models to access external data and tools, supporting standardized, secure access to files, databases, and APIs. They give AI agents the ability to interact with real-world applications, reduce hallucinations with accurate context, and offer stateful, multi-turn capabilities. Industry-standard architectures quickly evolved and adopted MCP to power agentic AI

We’re excited to share that AWS has been recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025. In this evaluation of 13 providers, AWS received the highest score in the Strategy category. We believe this recognition reflects our commitment to delivering flexible, cost-efficient AI infrastructure that helps you move from experimentation

Most organizations scaling their use of agents and tools hit the same challenges. Teams build in isolation, with no shared record of what exists, who owns it, or whether it’s been reviewed. The problem has moved from building agents and tools to discovering and governing them. AWS Agent Registry is purpose-built to solve this. Now

Teams that add Retrieval Augmented Generation (RAG) to a foundation model usually start with a single retrieval step against a single knowledge base. That works until the questions get harder, when the answer spans several sources, or the system has to decide which source to consult before it can respond. Enterprise agentic retrieval solves that:

Multi-tenant agentic chat assistants have become a frequent request for large-scale customers, and document chat sits at the top of the list. A user uploads a contract, a report, or a product manual, and then researches or asks questions about it immediately or in the future. The conversational interface is straightforward to build, but the