Artificial Intelligence
Category: Technical How-to
Customize Amazon Quick embedded chat into your application
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.
Implement vector-prompt document classification using Amazon Bedrock
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.
Improve contract search accuracy with auto-generated filters in Amazon Bedrock
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.
Custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge
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.
Building agentic workflows with SageMaker AI and Bedrock AgentCore
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.
Monitor on-premises and multi-cloud AI agents with AgentCore Observability
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.
Automate legacy web applications with Amazon Bedrock AgentCore Browser Tool
Learn how to automate legacy web applications that need human-like interaction using Amazon Bedrock AgentCore Browser Tool and Strands Agents. This walkthrough covers a reference architecture for an AI-powered digital worker that drives legacy interfaces through secure, isolated browser sessions while preserving human oversight and full audit trails.
Accelerating M&A due diligence with Amazon Bedrock AgentCore
Learn how to build a multi-agent M&A due diligence system on Amazon Bedrock AgentCore. This post walks through a reference architecture that combines agent orchestration, knowledge retrieval, and governance controls, then deploys a complete sample you can run in your own AWS account.
Part 2: Amazon Bedrock cost attribution with Amazon Athena and CUDOS
Learn how to visualize and analyze Amazon Bedrock cost attribution using Amazon Athena and CUDOS dashboards. This post shows how to set up CUR 2.0 with IAM principal data, query Bedrock spend by principal, project, and team, and build dashboards to track AI costs across your organization.
Run interactive IDEs on Amazon EKS with SageMaker AI to power up your AI workflows
The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito.








