Live intelligence

Latest AI updates

44 updates

Tutorial Editor selected

Use decision models with Amazon Bedrock AgentCore Evaluations

This post explains how to integrate decision models like Jev and Strands Decider 2B into Amazon Bedrock AgentCore Evaluations via code-based evaluators. It covers the trade-offs between decision models and LLM-as-a-judge, provides a step-by-step validation procedure, and recommends a cascade pattern where decision models screen turns and escalate low-confidence cases to built-in evaluators. The accompanying code sample demonstrates an HR Assistant agent with deterministic, built-in, and decision-model evaluators.

Why it matters: Provides a concrete, recent AWS tutorial with practical guidance on using decision models for agent evaluation, including validation steps and architecture, valuable for developers building AI evaluation pipelines.

AWS Machine Learning Blog20h agoScore 85

AI evaluationagent evaluationdecision models

Tutorial Editor selected

Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

This AWS blog post details a four-layer approach to context engineering for production AI agents, using the open-source Deep Insight multi-agent system as a reference. The layers are architecture-level context isolation, prompt-based output control, file-based context externalization, and verification with independent agents. Concrete code examples show a 95% token reduction in script context and a 97% reduction in system prompt size.

Why it matters: Provides actionable, code-backed techniques for managing LLM context windows in production, directly addressing a common bottleneck with measurable improvements.

AWS Machine Learning Blog22h agoScore 85

context engineeringmulti-agent architectureLLM context window

Tutorial Editor selected

Amazon Quick unifies structured and unstructured data querying

Amazon Quick now lets users connect S3-based unstructured documents and enriched structured datasets in a single Space, then query both through a custom chat agent. The walkthrough shows creating an S3 integration, building a knowledge base, enriching a CSV dataset with semantic context, and configuring an agent that answers cross-data questions with citations. It also covers generating artifacts like PowerPoints and sharing the agent.

Why it matters: Provides a concrete, recent tutorial on a new AWS feature that combines structured and unstructured data querying, valuable for data professionals.

AWS Machine Learning Blog1d agoScore 85

Amazon Quickunified data queryingknowledge bases

Tutorial Editor selected

ServiceNow on-behalf-of token exchange with Bedrock AgentCore Gateway

This post demonstrates how to configure Amazon Bedrock AgentCore Gateway for on-behalf-of (OBO) token exchange with Microsoft Entra ID, enabling AI agents to call ServiceNow REST APIs while preserving the user's identity. It covers setting up Entra application registrations, ServiceNow inbound OAuth integration, and AgentCore Gateway configuration with native OBO support. The pattern eliminates custom Lambda interceptors and applies to other OAuth-protected REST APIs.

Why it matters: Provides a concrete, recent technical guide for implementing identity propagation in AI agents, a critical enterprise requirement, with step-by-step configuration details.

AWS Machine Learning Blog1d agoScore 85

Amazon Bedrock AgentCoreOAuth 2.0AI agents

Tutorial Editor selected

Build a semantic layer for agentic AI with EDB Postgres AI and Amazon Bedrock AgentCore

This post demonstrates how to build a semantic layer for agentic AI using EDB Postgres AI, where business meaning is stored inside the operational Postgres database via comments, views, and semantic aliases. The layer is exposed to an external agent through an MCP endpoint, with governance enforced by Postgres roles, column-level grants, and row-level security. The architecture integrates with Amazon Bedrock AgentCore, and the post shows how different roles get different answers to the same question.

Why it matters: Provides a concrete, recent architecture for embedding a semantic layer in a database, with practical governance and observability details, valuable for AI engineers building agentic systems.

AWS Machine Learning Blog1d agoScore 92

semantic layeragentic AIdatabase governance

Tutorial Editor selected

Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI

This post presents a solution for ingesting web content into Amazon Bedrock Knowledge Bases using Crawl4AI, an open-source Python crawler, when the built-in Web Crawler connector lacks control over content extraction. The workflow crawls public websites, converts pages to Markdown with metadata sidecars, uploads to S3, and triggers ingestion into a managed or customer-managed knowledge base. It includes a CDK deployment guide and validation steps for retrieval quality.

Why it matters: Provides a concrete, actionable pattern for custom web ingestion into Bedrock Knowledge Bases, addressing a common RAG pain point with a sample implementation.

AWS Machine Learning Blog2d agoScore 85

Amazon Bedrock Knowledge BasesWeb crawlingRAG data ingestion

Tutorial Editor selected

Build efficient agents with knowledge distillation on Amazon SageMaker AI

This post demonstrates how to use sequence-level knowledge distillation (SeqKD) to fine-tune a smaller student model (Qwen3.5-4B) to replace a frontier teacher (GLM-5.3) for a shopping agent, reducing cost and latency. It covers generating high-quality trajectories via rejection sampling, encoding them for supervised fine-tuning, running serverless training on SageMaker AI, and evaluating the student with a low-cost pre-check before full end-to-end evaluation. The approach is practical for API-only teachers and includes code snippets for the entire pipeline.

Why it matters: Provides a concrete, actionable guide for a cost-saving AI technique (SeqKD) with a real-world agentic use case, including implementation details on AWS.

AWS Machine Learning Blog2d agoScore 92

knowledge distillationagentic AImodel efficiency

Tutorial Editor selected

AWS builds multi-agent engineering assistant with Nova Multimodal Embeddings and Bedrock AgentCore

AWS presents a multi-agent system for aerospace part substitution evaluation, combining Amazon Nova Multimodal Embeddings, Amazon Bedrock AgentCore, and the Strands Agents SDK. The system routes queries across three specialist agents, uses deterministic code for spec-fit scoring, and keeps engineers as final decision-makers. It supports multimodal retrieval and visual similarity scoring, with a human-in-the-loop design for audit trails.

Why it matters: This is a concrete, recent technical guide with architecture details and code, valuable for engineers building multimodal RAG and multi-agent systems.

AWS Machine Learning Blog2d agoScore 85

Amazon BedrockMultimodal AIAgentic AI

Tutorial

NVIDIA Cosmos Enables World Models for Interactive Simulation

NVIDIA Cosmos provides tools for physical AI developers to build world models for interactive simulation. It addresses challenges in adapting models to specific sensors, tasks, and data, and in deploying them into production. The blog highlights a workflow for creating customized world models.

AIHUB2d agoScore 75

NVIDIA Cosmosworld modelsphysical AI

Tutorial Editor selected

AWS publishes end-to-end Amazon Bedrock cost management playbook

AWS outlines a five-stage operating model for managing Amazon Bedrock generative AI costs: attribute spend to IAM principals, log requests with metadata, enforce budgets, optimize platform defaults, and run a review cadence. The playbook covers per-user attribution behind shared gateways, invocation logging with CloudWatch and S3/Athena, and three enforcement tiers ranging from alerts to synchronous gateway blocking. It also details model routing and prompt caching as central cost levers, with diagnostic queries for token usage by caller and workload.

Why it matters: It gives FinOps and platform teams a concrete, implementation-oriented framework for attributing, observing, and controlling Bedrock spend as agentic workloads increase token consumption.

AWS Machine Learning Blog5d agoScore 80

Amazon BedrockFinOpsAI cost governance

Tutorial

Amazon Bedrock AgentCore runtime adds session endpoints for live web app previews

AWS introduced session endpoints for Amazon Bedrock AgentCore runtime, giving each agent session an authenticated URL that forwards HTTP/1.1, HTTP/2 and WebSocket traffic to ports the developer explicitly allows. The post walks through a sample coding agent that keeps a Vite dev server running inside its microVM session, so developers can watch a site update in the browser as the agent saves files. It covers port configuration, SigV4 or JWT authentication, a local-endpoint proxy for browser clients, and the specific 403, 404 and 502 refusal codes the runtime returns.

AWS Machine Learning Blog5d agoScore 78

Amazon Bedrock AgentCorecoding agentsAWS

Tutorial

AWS Publishes Guide to Run ChatGPT Desktop App and Codex on Bedrock in GovCloud

AWS published a walkthrough for configuring the ChatGPT desktop app and Codex to use OpenAI models through Amazon Bedrock in AWS GovCloud (US-West). The setup routes inference through the Bedrock Mantle endpoint in us-gov-west-1 and relies on IAM, named AWS profiles, managed requirements.toml and config.toml policies, and network allow/deny rules. It covers authentication through a Bedrock API key or the AWS SDK credential chain, plus verification, troubleshooting, and rollback. AWS notes that following the guide does not by itself authorize a workload.

AWS Machine Learning Blog5d agoScore 78

Amazon BedrockAWS GovCloudOpenAI Codex

Tutorial

AWS Kiro: A Repeatable PHP-to-Java Migration Engine with Parity-First Workflow

AWS introduces a repeatable PHP-to-Java migration engine built on Kiro, an AI IDE, using skill files that encode an eight-phase, parity-first workflow. The engine includes a dependency-free discovery scanner that inventories models and flags ambiguities, plus a parity harness that verifies the Java port against the original PHP. The same skill files can run interactively in Kiro or at scale via AWS Transform, and the methodology is extensible to other frameworks and languages.

AWS Machine Learning Blog6d agoScore 78

AI-assisted migrationKiro IDEAWS Transform

Tutorial Editor selected

Building a Governed Agentic AI Platform on Amazon Bedrock AgentCore

This post presents an enterprise architecture for an Agentic AI Platform built on Amazon Bedrock AgentCore, addressing the operational challenges of scaling agents across teams. It details a federated operating model with a Platform control plane and repeatable Workstream cells, covering governed model and tool access, delegated delivery, fleet operations, and lifecycle management. The blueprint includes multi-account topology, service control policies, CI/CD pipelines with evaluation gates, and defense-in-depth security controls.

Why it matters: Provides a concrete, actionable reference architecture for enterprises seeking to operationalize agentic AI at scale, addressing real governance and delivery bottlenecks.

AWS Machine Learning BlogOct 29, 2026Score 85

Amazon BedrockAgentic AIEnterprise Architecture

Tutorial

Distributed training with Ray Train DLCs and EFA on EKS

AWS published a walkthrough for distributed PyTorch training on Amazon EKS using the Ray Train Deep Learning Container (DLC), KubeRay, and Elastic Fabric Adapter (EFA). The post demonstrates fine-tuning Qwen2.5-1.5B with FSDP across eight NVIDIA L4 GPUs on two g6.12xlarge instances without building any custom container image. It details the cluster architecture, configuration via environment variables, and deployment scripts that handle capacity discovery and infrastructure setup.

AWS Machine Learning BlogOct 29, 2026Score 78

distributed trainingAWS EKSPyTorch

Tutorial Editor selected

Fixing evaluation bias for model selection on Amazon Bedrock

AWS and Property Finder describe how 96.8% of evaluation labels had been inherited from the incumbent model, biasing comparisons against alternatives. They built expert-annotated ground truth and used iterative prompt optimization to select Llama 4 Maverick 17B Instruct on Amazon Bedrock. The post reports development-set Micro F1 rising from 0.55 to 0.81 and an estimated 86% cost reduction, with a caveat that evaluation-set scores are optimistic because prompts were tuned on included images.

Why it matters: It gives practitioners a concrete methodology for auditing biased evaluation labels and running fairer model selection, with reported accuracy and cost tradeoffs.

AWS Machine Learning BlogOct 28, 2026Score 88

Amazon Bedrockmodel evaluationprompt optimization

Tutorial Editor selected

AWS details multi-turn reinforcement learning on SageMaker AI

AWS published a hands-on guide to Multi-Turn Reinforcement Learning (MTRL) on Amazon SageMaker AI, which trains models on end-of-episode rewards rather than per-step labels. Agents hosted on Bedrock AgentCore (or EKS, EC2, Fargate) play multi-step tasks and return a single reward, with GRPO updating a LoRA adapter. In the guide's Wordle example, gpt-oss-20b improved its solve rate on held-out words from 61% to 84% at default reasoning effort and from 15% to 69% at low effort, at roughly $339 in

Why it matters: It gives practitioners a concrete, cost-transparent walkthrough of agentic multi-turn RL on a major cloud platform, including benchmark numbers, supported models and failure modes.

AWS Machine Learning BlogOct 24, 2026Score 83

AWSAmazon SageMaker AIMulti-Turn Reinforcement Learning

Tutorial

AWS CDK construct automates SageMaker AI endpoint auto scaling during updates

The AWS Machine Learning Blog describes a reusable CDK construct that replaces native Application Auto Scaling resources for SageMaker AI endpoints with three CloudFormation custom resources. The construct deregisters scalable targets before an endpoint update and re-registers the target and scaling policy afterward, avoiding update failures and silent loss of auto scaling. Asynchronous completion handlers wait for the endpoint to reach InService and the scalable target to become queryable. A Co

AWS Machine Learning BlogOct 24, 2026Score 71

AWSAmazon SageMaker AIAWS CDK