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AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. An earlier post covered AgentCore Identity credential management for AI agents. Running agents …

Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS Read More »

Direct communication between buyers and sellers outside approved channels can result in significant revenue loss annually while severely damaging brand reputation and destroying valuable business relationships. While messaging systems are essential for modern business operations and help provide rich customer insights, they can create significant risks when parties bypass the brokerage system to communicate directly. …

Intelligence-driven message defense and insights using Amazon Bedrock Read More »

Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views …

Beyond BI: How the Dataset Q&A feature of Amazon Quick powers the next generation of data decisions Read More »

Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement …

Introducing the agent performance loop: AgentCore Optimization now in preview Read More »

Every organization has access to the same foundation models. The real competitive advantage comes from customizing them with your proprietary data and domain expertise. But getting there is complex, even for experienced teams. It requires mastering fine-tuning techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning Verifiable Rewards (RLVR), navigating fragmented APIs …

Agent-guided workflows to accelerate model customization in Amazon SageMaker AI Read More »

Here are Google’s latest AI updates from April 2026 ​ 

Building meaningful dashboards demands hours of manual setup, even for experienced BI professionals. Amazon Quick now generates complete multi-sheet dashboards from natural language prompts, taking you from one or more datasets to a production-ready analysis in minutes. Data analysts building recurring operations reports, program managers preparing a leadership review, or engineers exploring a new dataset can …

Generate dashboards from natural language prompts in Amazon Quick Read More »

Organizations today are increasingly looking to combine analytics and AI to accelerate insights and decision-making. Amazon Quick, a unified agentic AI-powered analytics and decision intelligence service, brings together data visualization, natural language interaction, and agent-driven automation in a single, governed experience. With this, business users can explore data, generate insights, and take action without requiring …

From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick Read More »

Every BI team knows this bottleneck: a business user has a question that falls outside existing dashboards, so they file a ticket. An analyst writes the query, validates the results, and delivers them—hours or days later. Multiply that by hundreds of ad-hoc requests per month, and the backlog becomes the single biggest constraint on data …

Introducing Dataset Q&A: Expanding natural language querying for structured datasets in Amazon Quick Read More »

As organizations scale generative AI workloads in production, securing reliable GPU compute has become one of the most persistent operational challenges. Large language models (LLMs) and multimodal architectures demand specific instance types and when that capacity isn’t available, endpoints fail before they serve a single request. Building a real-time inference endpoint on Amazon SageMaker AI …

Capacity-aware inference: Automatic instance fallback for SageMaker AI endpoints Read More »