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Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to over-provisioning or performance bottlenecks, and the operational overhead of managing GPU resources efficiently. These pain points result in delayed time-to-market, suboptimal model performance, and inflated costs that can make […]

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Skills in Chrome let you discover, save and remix AI workflows — and repeat them instantly. ​ 

Agentic AI is transforming how organizations use generative AI, moving beyond prompt-response interactions to autonomous systems that can plan, execute, and complete complex multi-step tasks. While early proof of concepts in Agentic AI spaces excite business stakeholders, scaling them to production requires addressing scalability, governance, and security challenges. Amazon Bedrock AgentCore is an Agentic AI

Google is bringing people together in Washington D.C. at our AI for the Economy Forum. ​ 

Building effective reward functions can help you customize Amazon Nova models to your specific needs, with AWS Lambda providing the scalable, cost-effective foundation. Lambda’s serverless architecture lets you focus on defining quality criteria while it handles the computational infrastructure. Amazon Nova offers multiple customization approaches, with Reinforcement fine-tuning (RFT) standing out for its ability to teach

Amazon Bedrock regularly releases new foundation model (FM) versions with better capabilities, accuracy, and safety. Understanding the model lifecycle is essential for effective planning and management of AI applications built on Amazon Bedrock. Before migrating your applications, you can test these models through the Amazon Bedrock console or API to evaluate their performance and compatibility.

Now available through Amazon Bedrock AgentCore, use AWS Agent Registry to discover, share, and reuse agents, tools, and agent skills across your organization. As enterprises scale to hundreds or thousands of agents, platform teams face three critical challenges: visibility (knowing what agents exist across the organization), control (governing who can publish and what becomes discoverable

When you build AI-powered applications, your users must understand and trust AI agents that navigate websites and interact with web content on their behalf. When an agent interacts with web content autonomously, your users require visibility into those actions to maintain confidence and control, which they don’t currently have. The Amazon Bedrock AgentCore Browser BrowserLiveView

Stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime now enable interactive, multi-turn agent workflows that were previously impossible with stateless implementations. Developers building AI agents often struggle when their workflows must pause mid-execution to ask users for clarification, request large language model (LLM)-generated content, or provide real-time progress updates during long-running operations, stateless MCP