Agentic Data Operations Platform (ADOP): Data engineering into hours

The Agentic Data Operations Platform (ADOP) on AWS is a reference architecture designed to dramatically shorten the time required to stand up a new data source. Traditionally, data engineering teams spend weeks on tasks like writing ETL code, creating quality checks, updating semantic models, and verifying compliance. ADOP streamlines this process by leveraging specialized AI agents that automate the full lifecycle from Bronze to Gold.

Engineers no longer spend most of their time on pipeline plumbing; instead, they focus on shipping data products. Compliance is now applied inline during onboarding, rather than being a downstream gate. The architecture, not the model, governs how AI coding tools interact with data systems. This blog post is aimed at VPs of Engineering, Chief Data Officers, and Data Platform Directors, with platform engineering details provided later.

ADOP's unique approach involves running agents in development environments to reason, propose, and generate artifacts like ETL code, quality checks, semantic layer definitions, and regulation controls. Engineers review these outputs, and CI/CD promotes the generated artifacts into staging and production. In its default setup, production runs deterministic artifacts without invoking a model.

Organizations needing model-in-the-loop inference can extend this architecture using Amazon Bedrock endpoints, but the generated pipeline code remains static and auditable.

The platform is designed to optimize for cost predictability and audit posture, particularly for regulated data workloads. It encapsulates company standards within its design rather than relying on individual memory, ensuring consistent application of policies and regulations. ADOP caters to scenarios where data engineering velocity is hindered by manual onboarding and compliance overhead, such as enterprise data onboarding at scale and regulated pipelines in sectors like healthcare and finance.