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Amazon Bedrock AgentCore gateway is a fully managed, serverless AI gateway that provides a single, secure entry point for AI traffic. AgentCore gateway routes traffic to tools such as managed web search, managed knowledge bases, MCP servers, inference models (LLMs), agents (A2A, agents as tools, etc.), or HTTP endpoint. Today, we are announcing support for […]

Agents are becoming more autonomous and teams are running more of them, but trust and security have not kept pace. According to McKinsey, roughly 80% of organizations have already encountered risky behavior from AI agents. As a result, security and risk concerns are the leading barrier to scaling agentic AI (McKinsey’s State of AI Trust

As organizations move from experimenting with coding agents to adopting them across engineering teams, the leadership question changes. It is no longer only, “Can this tool help a developer?” It becomes, “How do we understand adoption, manage consumption, maintain reliability, and scale access responsibly?”. Codex can emit OpenTelemetry (OTel) metrics about its activity. When local

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