AI

Best practices for applying Amazon Bedrock Guardrails to code generation workflows

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

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Evaluating AI Agents: A production blueprint with Strands and AgentCore

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

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Building trade assistant: How Jefferies optimized front office trading operations with AI

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

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Building multi-Region visualizations with Highcharts in Amazon Quick

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.

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Detecting silent agent failures with Amazon Bedrock AgentCore optimization

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

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Agentic retrieval for Amazon Bedrock Managed Knowledge Base

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

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