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Today, we announce the availability of Claude Opus 5 on Amazon Bedrock and Claude Platform on AWS. Claude Opus 5 is Anthropic’s most advanced Opus model and the first in the fifth generation. It is a meaningful step forward, providing improvements across the workflows that teams run in production such as agentic coding, knowledge work, […]

Building a deep learning-based explainable next-best-product recommendation system helps banking institutions predict which product a customer needs next. Banks hold vast amounts of customer data, including transaction histories, product ownership records, demographic profiles, and behavioral patterns. Translating this data into actionable, personalized product recommendations remains a significant challenge. Traditional rule-based systems and collaborative filtering approaches

This post is co-written with Chris Dickens from OpenAI. Developers building agentic coding, long-horizon reasoning, and high-volume inference workloads want frontier models they can call through familiar APIs, without operating separate model infrastructure. OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock. The three models cover workloads from autonomous coding agents

This post continues our series on best practices with Amazon Bedrock Guardrails. For the previous post, see Build safe generative AI applications like a pro: best practices with Amazon Bedrock Guardrails. AI-powered coding assistants and code generation workflows, such as Claude Code, Kiro, and OpenAI Codex, are transforming how developers write software. These tools generate

This post was co-written with Motorway and the AWS Prototyping and AI Customer Engineering (PACE) team. Motorway, a UK-based online car marketplace, runs a daily auction where up to 8,000 dealers bid on up to 2,500 vehicles. Motorway worked with AWS Prototyping and AI Customer Engineering (PACE) to build an AI-powered dealer stock search agent

If you manage a front office trading desk at investment banks, you know the challenge: traders need real-time insights into client behavior, trade patterns, and market trends from vast amounts of data to make split-second decisions. However, they rarely have the time during the day, nor the coding ability, to build and maintain systems capable

When your carrier performance data spans multiple regions, your dashboard must reconcile fundamentally different competitive structures within a single view. For example, in the US, you rank three carriers (Carrier 1–3) across 49 states and hundreds of metro markets. In the UK, you’re comparing four carriers (Carrier 4–7) across a separate set of national regions.

If you’re operating AI agents at scale, Amazon Bedrock AgentCore surfaces a category of insights you’ve probably experienced but struggle to detect: your dashboards show green across the board. 99% completion rate, healthy latency, zero error spikes. And yet customer complaints trickle in about incorrect outcomes. An order modification that was never actually executed. A

Your users ask multi-part, comparative, and exploratory questions that span PDFs, slides, tickets, transcripts, and web content. Classic single-shot retrieval breaks down on these questions. Answers miss context, support tickets escalate, and analysts waste hours re-running searches. Agentic retrieval for Amazon Bedrock Managed Knowledge Bases is designed for these questions. Consider two questions an analyst

AI Teammates are agentic AI on Amazon Bedrock, and few engineering organizations run them in production at the scale that monday.com does. Nine in ten Builders use AI coding tools every month, up from roughly half a year ago. Per-engineer PR throughput is up by more than half. Every figure in this post comes from