Official announcement · AWS Machine Learning ·
Agentic assistants built on frontier models run a multi-step tool loop, and paying frontier prices on every step is hard to sustain at production scale.
A tool or model opens in the Heidelberg AI Curriculum: what it does, what it costs, how to start. A repo opens on GitHub.
OfficialAWS Machine Learning
This post shows how to use Crawl4AI, an open source web crawler, as a content preparation layer for Amazon Bedrock Knowledge Bases, handling JavaScript rendering, selective extraction, and custom metadata enrichment before web content reaches an Amazon S3 data source.
OfficialAWS Machine Learning
Learn how to build a multi-agent engineering assistant that compresses aerospace component substitution evaluation from days to minutes, using Amazon Nova Multimodal Embeddings, Amazon Bedrock AgentCore, and the Strands Agents SDK to assemble substitution evidence while keeping the engineer as the decision-maker.
OfficialAWS Machine Learning
Circle K's DevOps team cut incident triage from 45-60 minutes to under 15 by building an agentic triage system on Amazon Bedrock. This post walks through the architecture, the initial Amazon Bedrock Flows implementation, the migration to Amazon Bedrock AgentCore, and the lessons learned from scaling it.
OfficialAWS Machine Learning
This post presents a five-stage operating model for managing Amazon Bedrock cost: attribute every dollar to an owner, make every request observable, enforce budgets, optimize platform defaults, and operate a review cadence. Written for FinOps and platform engineering teams.