AWS Public Sector Blog
Author: Phillip Spies
Phillip Spies is a senior solutions architect on the AWS Federal Civilian team with 20 years of development and solution architecture experience. He specializes in cloud infrastructure design, container technologies, and generative AI adoption, holding multiple AWS certifications. Phillip builds production-grade generative AI prototypes with government agencies, helping organizations accelerate from concept to deployed capability through cloud migrations and application modernization.
Distributed generative AI for government
A distributed approach, one that brings generative AI to the data rather than the reverse, is achievable today using Amazon Web Services (AWS) solutions such as Amazon Bedrock for orchestration, Amazon Neptune for data lineage, and AWS Identity and Access Management (IAM) for source-point security enforcement.
The Signal-Activated Agent Pattern: A reference architecture for proactive government AI
In Part 2 of our two-part series, we discuss the architecture of the Signal-Activated Agent Pattern and its application for proactive government AI. For Part 1, see Signal-activated generative AI: How agencies can reach more people and react faster.
Signal-activated generative AI: How agencies can reach more people and react faster
This two-part series introduces the Signal-Activated Agent Pattern—an architectural approach, backed by a deployable Amazon Web Services (AWS) reference implementation, that helps government AI platforms move from generic, reactive question-answering to proactive, contextually personalized decision support. With this solution, agencies can reach more people, respond faster, and deliver the right value to the right official at each decision point.
Implementing per-user token guardrails for Amazon Bedrock in government agencies
This post presents two complementary patterns for implementing per-user token guardrails on Amazon Bedrock from Amazon Web Services (AWS).
TOLAP: Closing the data-object security gap in AI agent architectures
Every major agent framework has a security model for this. Amazon Web Services (AWS), Microsoft, and Google each ship agent solutions with authentication and credential management built in. Amazon Bedrock Agents, for example, enforces AWS Identity and Access Management (IAM)-based authorization on which AWS Lambda functions, Amazon Simple Storage Service (Amazon S3) buckets, and Amazon Bedrock Knowledge Bases an agent might invoke.
Building an AI-powered scientific meeting transcription platform with AWS
In this post, we explore how to build a sophisticated meeting transcription and analysis platform using AWS services, designed specifically for the scientific community. Our solution combines the power of AWS Transcribe, Amazon Bedrock, and other Amazon Web Services (AWS) services to create an intelligent tool that transforms how researchers document and analyze their discussions.