AWS releases catalog of 23 reusable generative AI assets

AWS has published a catalog of 23 reusable generative AI assets, giving teams deployable code and implementation guidance for document processing, customer support, AI agents, retrieval, development and cloud operations.

The release packages implementation work that teams would otherwise have to assemble themselves. Five assets cover document processing and extraction, five cover contact centers and voice, four focus on agentic AI and chatbots, four on retrieval-augmented generation and knowledge bases, three on development and migration, and two on infrastructure and operations.

The entries combine source code, deployment documentation and infrastructure-as-code. Use cases range from document ingestion and extraction to voice and text agents, full-stack agent templates, reranking, semantic caching, model migration, Amazon Bedrock logging and AI-assisted business intelligence.

AWS describes all 23 assets as open source, production-ready, security-reviewed, validated, maintained and tested in production environments. The public repositories inspected for this story include security policies and detailed deployment material, but no common audit showing that every asset received the same review or maintenance treatment. The open-source claim was not checked license by license across the catalog; two inspected agentic repositories use Apache-2.0 and MIT-0 licenses.

The Contact Center GenAI Agent combines Amazon Connect, Amazon Lex, Amazon Bedrock Knowledge Bases and a retrieval-augmented generation stack for voice and text support. Its repository uses AWS CloudFormation and includes optional asynchronous hallucination detection.

The accelerated intelligent document processing repository implements a serverless optical-character-recognition and generative AI pipeline with two processing modes, queuing, monitoring, configuration profiles, human review and evaluation features. It supports CloudFormation with AWS CDK, along with experimental Terraform and OpenTofu deployment options.

The Fullstack AgentCore Solution Template pairs a React interface and Amazon Cognito authentication with an Amazon Bedrock AgentCore backend. It supports multiple agent frameworks and CDK or Terraform infrastructure. The Agentic Chatbot Accelerator adds text and voice interfaces, four agent patterns, Model Context Protocol server and knowledge-base integrations, evaluation, feedback and observability, with CDK and Terraform deployment options.

AWS says the full-stack template can deploy in under 30 minutes and the contact-center walkthrough can be completed in under an hour. Those timings were not independently reproduced and depend on prerequisites, account configuration and permissions.

An AWS case study on DoorDash says its contact-center work was ready for live testing in two months, cut application development time by 50%, kept response latency at 2.5 seconds or less and increased automated testing capacity 50-fold. DoorDash and AWS reported the figures; they were not independently measured for this story.

A case study co-written by AWS and Ricoh says the document-processing system reduced onboarding from four to six weeks to two to three days and lowered engineering effort from about 80 hours to under five per deployment. The November catalog describes a sevenfold throughput increase from 10,000 to 70,000 documents per month. The March case study’s prose instead says current processing exceeds 10,000 documents per month and that the infrastructure can scale to 70,000, while its table lists more than 70,000 as the after-state.