Formula 1® Uses Agentic AI on AWS to Accelerate Data Operations

Formula 1 engages over 800 million fans worldwide through various digital platforms, requiring rapid data processing to support real-time fan engagement and marketing strategies. To address operational challenges, F1 collaborated with AWS in early 2026 to build the Data Accelerator, a solution that employs agentic AI on Amazon Bedrock AgentCore.

The platform's core challenge was the manual and time-consuming process of onboarding new data sources, which previously took 6 to 8 weeks per source. Additionally, frequent upstream data changes and fragmented visibility hampered efficient operations. The Data Accelerator was designed to automate and streamline these processes.

The solution comprises five key workstreams: agentic data source onboarding, automated schema evolution detection, unified data access via Amazon SageMaker Unified Studio, end-to-end observability with root cause analysis, and optimized customer identity resolution algorithms.

The onboarding process is driven by platform agents that generate complete production-ready pipelines from minimal input, such as a Business Requirements Document (BRD). These agents operate in two phases: configuration generation and full pipeline creation, including infrastructure, transformations, and governance policies, all without manual coding.

A significant feature is integrated GDPR classification, where agents analyze data columns for personal or sensitive information, tagging them accordingly for compliance. The system's modular architecture allows new capabilities to be added as skill modules, maintaining maintainability and scalability.

Schema evolution monitoring enables the system to detect and automatically update pipelines in response to upstream data structure changes, reducing resolution times from days to hours. Data access is unified through Amazon SageMaker Unified Studio, providing a centralized, governed environment for data scientists and engineers.

Enhanced observability offers full data lineage visualization and causal root cause analysis, allowing stakeholders to quickly identify issues and understand their origins. The platform also includes an optimized customer identity resolution process, which now runs twice as fast, enabling more timely and relevant personalization.

Security and governance are embedded into the architecture, with strict access controls, audit trails, human review processes, and network isolation, ensuring production readiness.

The implementation has delivered substantial operational improvements: onboarding time decreased from 6-8 weeks to approximately 40 minutes, with 95% of tasks handled autonomously by AI agents. The backlog of data source integrations was cleared in weeks, and data engineers can now focus on strategic initiatives.

The success of the Data Accelerator hinges on principles of developer-centric design, continuous governance integration, and maintaining human oversight. This approach is replicable across organizations facing similar multi-source data challenges.

For those interested in adopting similar solutions, AWS offers services such as Amazon Bedrock AgentCore, SageMaker Unified Studio, and tools like Kiro, supported by AWS Professional Services.

This initiative exemplifies how agentic AI can revolutionize data operations, providing faster, more reliable, and compliant data management at scale.