Parvinder Singh
Kijoon Lee
Suntosh Murthy
Moving Generative AI from simple experiments to production-scale AI agents introduces major challenges for data teams: complex data pipeline delays, disconnected enterprise systems, and unpredictable cloud compute costs. To build autonomous agents that can accurately reason and take action, organizations need real-time data access across their core platforms without the risk and expense of constantly moving data.
Join AWS and Denodo for a practical deep dive into the data strategies required to scale advanced AI capabilities. We will showcase how to connect foundation models with your distributed enterprise data landscape using Amazon Bedrock, Amazon Quick, and the Denodo Platform.
Discover how a unified data layer delivers up to 10x faster performance, slashes operational expenses, and establishes the clear security boundaries needed to safely scale AI agents from pilot to enterprise reality.
What You Will Learn:
- Live Data Access Across Enterprise Sources: How to connect AI agents directly to core systems like SAP, Oracle, and OSIsoft PI to deliver real-time insights without the latency of traditional data copying.
- Controlling AI Costs & Performance: Practical strategies to maximize system performance and reduce recurring AI token expenses by delivering precision data directly to the model.
- Enforcing Trusted Governance: How to safely deploy autonomous workflows using dynamic data masking and access controls that protect corporate intellectual property and maintain compliance audit trails.
- Use-Case Demonstration: A look at an autonomous agent successfully connecting real-time operational layers, core business applications, and customer digital channels simultaneously.
