Artificial Intelligence

Category: Amazon Bedrock

Scaling agentic AI: Enterprise patterns without vendor lock-in

Scaling agentic AI: Enterprise patterns without vendor lock-in

Scaling agentic AI across an enterprise requires patterns that preserve flexibility while avoiding vendor lock-in. In this second post of our multi-agent series, we examine how ML teams operate many agentic AI systems across a multi-everything environment of frameworks, models, and providers, and the principles that let those systems scale together.

Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

Learn how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Purpose-built AI agents handle discovery, infrastructure as code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes.

Domain and publish date filters for Web Search on AgentCore

Domain and publish date filters for Web Search on AgentCore

Web Search on Amazon Bedrock AgentCore now supports runtime domain and published-date filtering. New per-request filters give developers per-call control over which web sources their agents consult and how fresh those sources must be, all enforced server-side. This release also expands Web Search to the Europe (Ireland) and Asia Pacific (Tokyo) Regions.

Asynchronous patterns for calling Amazon Bedrock AgentCore agents in serverless pipelines

Asynchronous patterns for calling Amazon Bedrock AgentCore agents in serverless pipelines

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.

How Fanatics Betting and Gaming built a multi-agent customer support system

How Fanatics Betting and Gaming built a multi-agent customer support system

Fanatics Betting and Gaming built a multi-agent customer support system on AWS to handle the complexity of sports betting: state-specific rules, real-time responsible gaming, and traffic spikes during major sporting events. This post walks through the architecture, the AWS services involved, and the patterns for your own multi-agent support solution.

KnowledgeForge: mining gold from the ITSM ticket graveyard

KnowledgeForge: mining gold from the ITSM ticket graveyard

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.

Implement vector-prompt document classification using Amazon Bedrock

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.