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Production artificial intelligence (AI) agents can fail silently. They may return plausible but incorrect answers, enter infinite reasoning loops, or select the wrong tools without triggering error alerts. These failures make debugging production agent behavior difficult because standard logs and metrics do not capture how decisions are made. Amazon Bedrock AgentCore Observability addresses these debugging […]

A Google expert explains what it means to take a full-stack approach to AI and why it’s been the foundation of our AI work for so long. ​ 

Picture this: a compliance officer needs a specific clause during an audit, an attorney needs contract terms while a client waits on the phone, or a finance analyst needs numbers from last quarter’s report before a meeting that starts in 10 minutes. In each case, waiting for a scheduled job to finish is not practical.

Insurance is an $8 trillion global industry burdened by manual workflows and a growing talent shortage. Cara delivers an AI-native solution on AWS that automates back-office processes for insurance brokerages. Insurance agents routinely spend hours on repetitive tasks. These include completing applications, analyzing policy coverages, re-keying data across systems, and relaying information between clients and

This post is co-written by Christopher Phillippi and Chrissie Cui from Stripe. Stripe processes $1.4 trillion in annual payment volume across 50 countries, requiring compliance teams to review thousands of transactions daily. This post explores how Stripe built a production-grade AI agent system on AWS using Amazon Bedrock that reduced review handling time by 26

The opinions expressed in this post are the authors’ views and not those of Cisco. Enterprise architectures have long been centered on REST APIs and microservices. These systems are stable, well-tested, and deeply embedded in production environments. They weren’t designed for Agent-to-Agent (A2A) communication, the emerging standard for autonomous agents that collaborate, reason, and coordinate

Optimizing model training on Amazon SageMaker AI with NVIDIA Blackwell GPUs changes what’s practical for large AI models. If you train large models today, you are likely working around a familiar set of constraints: batch sizes limited by GPU memory, sequence lengths cut short to avoid out-of-memory errors, and model sharding that adds communication overhead

As display technologies advance to higher resolutions, many organizations face a common challenge: their existing video libraries contain lower-resolution content that appears pixelated or blurry on modern high-definition displays. Traditional video upscaling approaches often struggle with computational limits, inconsistent quality, and scalability issues when processing large video collections. Many existing solutions also lack the techniques

On a typical Monday morning, an enterprise operations team receives multiple AWS Health notifications about Amazon Linux 2 end-of-life, RDS version deprecations, and EC2 instance retirements across 50+ accounts. Without self-service analytics, the team has no way to quickly identify the events that affect production systems, the events that require immediate action versus long-term planning,

When a customer service agent autonomously queries order databases, retrieves return policies, and synthesizes answers, it needs governed access to multiple data sources across your organization. Building agentic AI applications on a modern data mesh requires fine-grained access control enforced at every layer of the data interaction chain. AI agents that autonomously discover database schemas,