Category: Artificial Intelligence

Amazon Bedrock AgentCore services are now being supported by various IaC frameworks such as AWS Cloud Development Kit (AWS CDK), Terraform and AWS CloudFormation Templates. This integration brings the power of IaC directly to AgentCore so developers can provision, configure, and manage their AI agent infrastructure. In this post, we use CloudFormation templates to build an end-to-end application for a weather activity planner.

In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock.

PDI Technologies is a global leader in the convenience retail and petroleum wholesale industries. In this post, we walk through the PDI Intelligence Query (PDIQ) process flow and architecture, focusing on the implementation details and the business outcomes it has helped PDI achieve.

In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process.

This blog post explains how TR’s Platform Engineering team, a geographically distributed unit overseeing TR’s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore.

In this post, we walk you through the complete architecture to structure and store episodes, discuss the reflection module, and share compelling benchmarks that demonstrate significant improvements in agent task success rates.

In this post, we show how bunq upgraded Finn, its in-house generative AI assistant, using Amazon Bedrock to transform user support and banking operations to be seamless, in multiple languages and time zones.

In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta’s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code.

In this post, we’ll guide you through building multimodal RAG applications. You’ll learn how multimodal knowledge bases work, how to choose the right processing strategy based on your content type, and how to configure and implement multimodal retrieval using both the console and code examples.

In this post, we show you how fine-tuning enabled a 33% reduction in dangerous medication errors (Amazon Pharmacy), engineering 80% human effort reduction (Amazon Global Engineering Services), and content quality assessments improving 77% to 96% accuracy (Amazon A+). This post details the techniques behind these outcomes: from foundational methods like Supervised Fine-Tuning (SFT) (instruction tuning), and Proximal Policy Optimization (PPO), to Direct Preference Optimization (DPO) for human alignment, to cutting-edge reasoning optimizations such as Grouped-based Reinforcement Learning from Policy Optimization (GRPO), Direct Advantage Policy Optimization (DAPO), and Group Sequence Policy Optimization (GSPO) purpose-built for agentic systems.