Artificial Intelligence · 12.08.2026, 15:25 UTC
How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | AWS Machine Learning ↗ |
| Veröffentlicht | 12.08.2026 UTC |
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This post is co-authored with OneAdvanced team Deploying AI agents on a United Kingdom (UK)-sovereign AWS architecture requires careful decisions about model hosting, data residency, and agent orchestration. OneAdvanced, a UK-based enterprise software provider serving over 10,000 customers, needed to deliver AI capabilities while making sure that no data would leave the UK. At the time, the specific models they wanted, Llama 4 Maverick and Llama Guard 4, weren’t yet available through managed services in the UK region. OneAdvanced took a different path: self-hosting open-weight large language models (LLMs) on AWS infrastructure they fully control. In this post, we describe how OneAdvanced built a UK-sovereign AI solution using Llama 4 Maverick and Llama Guard 4 on Amazon SageMaker AI. The solution pairs a Retrieval Augmented Generation (RAG) pipeline backed by Amazon Aurora PostgreSQL-Compatible Edition with the pgvector extension, over 50 specialized agents powered by Strands Agents SDK, and a tool layer running on Amazon Elastic Container Service (Amazon ECS). We walk through the architecture, key implementation decisions, and results. The challenge: Data sovereignty and model hosting OneAdvanced provides sector-focused software as a service (SaaS) solutions to organizations in healthcare, legal, and many other regulated industries. Their customers handle sensitive data daily, including patient records, legal case files, and compliance documentation. These customers expect that AI tooling meets strict data residency, security, and privacy standards. As Andrew …