Artificial Intelligence
Inference meta-monitoring for Amazon SageMaker AI endpoints with Amazon Quick
Learn how to build an inference meta-monitoring system for Amazon SageMaker AI endpoints using Amazon Quick. This governance layer sits above production ML inference pipelines to continuously track prediction and data quality, detect drift, integrate delayed ground truth, and surface automated performance dashboards.
Introducing explicit prompt caching for OpenAI GPT-5.6 models on Amazon Bedrock
OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, along with explicit prompt caching that gives you precise control over which parts of your prompt are cached and reused. Learn how to get started, set up explicit caching, and migrate existing GPT workloads to reduce inference cost.
Migrate your prompts to new models and optimize them on Amazon Bedrock
Amazon Bedrock Advanced Prompt Optimization optimizes your prompts for up to 5 models at once and compares original versus optimized performance across quality, latency, and cost. Migrate to a new model or improve your current one in minutes instead of weeks.
Authenticate with Private Key JWT using Amazon Bedrock AgentCore Identity
This post explains how Private Key JWT client authentication works in AgentCore Identity and reviews the supported grant flows. We then walk through creating an AWS KMS signing key, registering its public key with your identity provider, configuring a credential provider on the AWS Management Console, and reviewing example AWS CloudTrail events that record your agent’s access.
Generate Autonomous Business Insights with AI Agent and MCP Servers
Learn how Amazon Bedrock AgentCore delivers autonomous, cross-system business intelligence through configuration rather than custom code. Using pre-built MCP server connectors, fine-grained access control, and persistent memory, enterprises can query multiple data sources with natural language while enforcing role-based boundaries automatically.
Automating customer retention workflows in Amazon Quick
Learn how to build a no-code customer retention pipeline in Amazon Quick that detects at-risk customers from call transcripts and CSAT data, scores them by retention priority with a custom MCP Action, and generates personalized retention letters, reducing response time from days to minutes.
How AgentCore Gateway supports the MCP 2026-07-28 spec
The Model Context Protocol (MCP) published its 2026-07-28 specification, the largest revision since launch: MCP is now stateless, with a governed extensions system and hardened authorization. Learn what changed and how to enable the new version on Amazon Bedrock AgentCore Gateway with a single UpdateGateway call.
Market surveillance agent with LangGraph and Strands on AgentCore
Learn how to architect and deploy a production-ready multi-agent AI system using LangGraph for workflow orchestration and Strands for agent reasoning on Amazon Bedrock AgentCore. This post walks through a market surveillance example with state-driven orchestration, checkpoint-based recovery, and AgentCore memory and observability.
Beyond RAG: Task-aware knowledge compression for enterprise AI on AWS
Traditional RAG hits a ceiling on analytical tasks that span hundreds of documents. This post shows how to use task-aware knowledge compression (TAKC) on AWS to pre-compress entire knowledge bases into task-specific representations, cache them at multiple fidelity tiers, and route each query to the right tier, with an open-source implementation you can deploy.
Deepgram enhances Amazon SageMaker AI support with AWS IAM Temporary Delegation
In this post, we cover why Deepgram built on IAM temporary delegation, how the integration works end-to-end, and what it unlocks for customers running Deepgram speech models on SageMaker AI. With this integration, Deepgram has reduced the time for initial investigation on a SageMaker AI support ticket from days to minutes.









