Artificial Intelligence

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.

Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. They partnered with the AWS Generative AI Innovation Center (GenAIIC) to develop an automated log classification pipeline powered by Amazon Bedrock. In this post, we discuss how Amazon Bedrock, through Anthropic’ s Claude Haiku model, and Amazon Titan Text Embeddings work together to automatically classify and analyze log data. We explore how this automated pipeline detects critical issues, examine the solution architecture, and share implementation insights that have delivered measurable operational improvements.

The AWS AI League, launched by Amazon Web Services (AWS), expanded its reach to the Association of Southeast Asian Nations (ASEAN) last year, welcoming student participants from Singapore, Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. In this blog post, you’ll hear directly from the AWS AI League champion, Blix D. Foryasen, as he shares his reflection on the challenges, breakthroughs, and key lessons discovered throughout the competition.

In this post, we demonstrate how to use a GitHub Actions workflow to automate the deployment of AI agents on AgentCore Runtime. This approach delivers a scalable solution with enterprise-level security controls, providing complete continuous integration and delivery (CI/CD) automation.

In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org.