Category: Technical How-to

Learn how to add context-aware security monitoring to FHIR APIs using Amazon Bedrock. This post shows how to detect anomalous access patterns, classify data sensitivity automatically, and generate compliance reports in natural language, all without adding latency to clinical workflows.

Classifying, extracting, and validating high volumes of documents is a challenge across banking, insurance, healthcare, and the public sector. See how a mid-size mortgage lender automates its entire document intake pipeline, from email to validated data, using the AWS GAIIC IDP Accelerator and Amazon Quick Automate.

In this post, you learn three serverless patterns (task-token callback, direct service integration, and durable functions) for invoking Amazon Bedrock AgentCore agents asynchronously from AWS Step Functions pipelines, eliminating idle compute costs while your AI agent processes requests.

KnowledgeForge mines resolved ITSM incident tickets into new knowledge base articles and automatically curates the existing library by deduplicating, quality-scoring, and improving content, using Amazon Bedrock, Amazon S3 Vectors, and AWS Step Functions in a multi-tenant, closed-loop pipeline.

Amazon Quick embedded chat brings a conversational AI interface into your web application. This post walks through customizing the embedded chat with container and SDK styling, branding removal, and a custom agent persona so it matches your brand’s look, feel, and voice.

Learn how to build a multi-agent document classification solution on Amazon Bedrock using the Strands Agents SDK. Three specialized agents combine textual analysis with Claude Haiku 4.5 and visual similarity search with Amazon Titan Multimodal Embeddings to accurately classify insurance documents such as policies and affidavits.

In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search accuracy.

In multi-turn reinforcement learning, your custom reward function decides what the model actually learns. This post shows how to design a composite multi-turn reward for Amazon Nova Forge, execute model-generated code safely inside it, and instrument each component to catch the pitfalls that quietly collapse a reward.

Learn how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime to build a multi-agent workflow where each specialized agent uses the model best suited to its job. This post also shows how to get token-level observability from SageMaker endpoints that Strands Agents does not instrument by default.

Set up Amazon Bedrock AgentCore Observability for AI agents running outside AWS: on-premises, on GCP, on Azure, or on developer machines. This walkthrough uses the AWS Distro for OpenTelemetry (ADOT) and IAM credentials to route session traces, span metrics, and token usage to the same AgentCore Observability dashboard.