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A US-headquartered global organization recently came to us with a deceptively simple data-residency request: let their engineers use Claude Code. The requirement: Amazon Bedrock model inference had to be processed in London (the eu-west-2 AWS Region), not merely called from London. Their compliance team had drawn a hard line. Prompts, completions, and intermediate processing were […]

Teams that adopt Amazon Bedrock Automated Reasoning checks often want to run the policy lifecycle in code. Running it in code keeps the work repeatable, reviewable, and driven by the coding agent they already use. Authoring a good Automated Reasoning policy has a learning curve, and the lifecycle has constraints that can trip you up.

Many enterprises have a long tail of internal tools that never get built. A team needs a shipping-cost calculator, a straightforward intake form, a small dashboard over a spreadsheet, but each one requires a developer, a backlog slot, and a deployment pipeline. The tools are too small to prioritize and too numerous to ignore. PDI

Optimizing generative AI inference deployments requires benchmarking endpoints, evaluating instance configurations, and iterating on deployment settings. The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook workflow. These recommendations are also accessible through the Amazon SageMaker AI UI and Boto3 APIs. With this release, you

Buying a home is one of the biggest financial decisions most people face, and LendingTree built a multi-agent mortgage assistant on Amazon Bedrock to make the process more straightforward. The assistant educates borrowers, understands their situation, and provides tailored options in a natural conversation. Borrowers must weigh purchase or refinance, conventional or government-backed, 15-year or

What if deploying production-grade AI agents required zero infrastructure management, came with enterprise observability built-in, and worked easily with your existing on-premises systems? Mobileye, the autonomous driving pioneer with more than 230 million EyeQ system-on-chips deployed across roughly 1,200 vehicle models worldwide, saw an opportunity to free skilled engineers from routine internal ticket status inquiries.

Our agent runs in the cloud, but our users’ spreadsheets live on their laptops. How do you bridge that gap? The Model Context Protocol (MCP) is an open source standard introduced by Anthropic in November 2024 to standardize how AI models connect to external data and tools. MCP follows a client-server architecture where an MCP

If you build and automate workflows in n8n without writing much code, you’ve probably used its AI Agent node to add a model call to a workflow. It’s a great start. But a production agent needs more than a single model call: memory that lasts beyond one run, tools it can actually use like a

When a foundation model needs to answer a question about last week’s earnings call, yesterday’s regulatory change, or this morning’s weather forecast, it needs knowledge it was never trained on. Grounding the model in current web knowledge closes that gap – whether it’s powering chatbots, coding assistants, CLI tools, or enterprise applications, grounding helps answer

Extracting insights from dozens of websites often means manually checking each one, a process that quickly becomes overwhelming. Design teams need to track competitor products, marketing teams want to monitor content trends, and product managers need to stay on top of market intelligence. But doing this manually means someone has to visit sites, copy content,