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Accelerating product development with AI-driven lifecycle management (AI-PDLC) in AWS Quick

Learn how to build an AI-driven product development lifecycle (AI-PDLC) with a suite of six AWS Quick agents that take you from scattered customer signals to a validated prototype across Discover, Decide, Design, and Prototype, with human approval at every gate and a specification ready to hand to coding agents like Kiro.

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AWS Machine Learning Blog

Adobe’s AI experiments let you walk around in photos and interview your characters

Some of the most impressive tech that Adobe showcases at its annual Max conference each year is the experimental features it's developing, and the latest of those tools are being announced today: they can transform images into explorable 3D scenes, design AI characters by talking to them, and turn photo editing into a copy/paste process. […]

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The Verge · AI

Amazon Bedrock tokenomics data lake

Running Amazon Bedrock across many accounts shows you the bill but not what happened inside each call. This post builds a centralized observability data lake that aggregates Bedrock invocation logs from every linked account into one queryable table, so you can see prompts, token counts, model choice, and per-invocation cost across your organization.

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AWS Machine Learning Blog

Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore

Learn how Trovata built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using an RBAC-secured architecture for bank-branded AI deployment and agentic workflows that move corporate treasury from manual operations to real-time decision-making at enterprise scale.

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AWS Machine Learning Blog

Build a daily dependency watcher with LangGraph and Amazon Bedrock AgentCore web search

The Web Search API in Amazon Bedrock AgentCore gives agents current, cited web results signed with the AWS credentials they already use. In this post, build a daily dependency watcher as a LangGraph graph that researches package ecosystems in parallel, keeps its rules in code, and checks every finding against its cited sources.

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AWS Machine Learning Blog