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This is a guest post co-written with Vitaly Omelchenko from the TReNDS Center at Georgia State University. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), a joint center of Georgia State University, Georgia Institute of Technology, and Emory University, we develop and apply advanced analytical methods and neuroinformatics tools for brain […]

As the National Hockey League (NHL) regular season enters its final stretch each spring, one question dominates the minds of hockey fans: has my team clinched the playoffs? The answer is often surprisingly hard to discern. With 32 teams, complex tie-breaking rules, and hundreds of remaining games, determining whether a team is mathematically guaranteed a

Before AI agents, it was generally sufficient for access controls to treat each action as an independent event. Applications relied on deterministic business logic to enforce whether actions happened in the right order or whether the data was up-to-date. AI agents behave in fundamentally different ways than traditional applications. They decide at runtime which tools

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