Distributed Databases: Enabling Agentic AI Across Global Regions

The Agentic AI Infrastructure Challenge

Agentic AI systems represent a paradigm shift from traditional AI. While conventional AI responds to prompts, agentic systems operate autonomously, making decisions and taking actions across multiple data sources in real-time. These agents place unprecedented demands on data infrastructure, requiring agent parallelism for high-volume interactions, AI vector search across massive data sets, and transactional capabilities on back-end systems.

The challenge intensifies when considering global deployment of this technology. Agentic AI workloads are highly variable in resource consumption, making traditional fixed-capacity architectures inadequate and unaffordable for smaller organizations. More critically, these systems need always-on unified access to distributed data while respecting data sovereignty regulations that require keeping data within specific geographic boundaries.

Data Sovereignty: The Regulatory Reality

Agentic AI deployment must navigate complex data sovereignty requirements. GDPR mandates that EU citizen data remain within European borders and banking regulations in India require that customer information stay in the country. There are similar regulations in many other jurisdictions. This need for data sovereignty creates fragmented global data landscapes. For agentic AI systems requiring comprehensive data access to make intelligent autonomous decisions, this regulatory environment poses significant architectural challenges. Traditional approaches often require separate AI applications and vector database deployments in each region--which can dramatically increase costs and complexity while limiting systems' ability to leverage global insights.

Serverless Architecture: Democratizing Advanced Capabilities

Serverless distributed database architectures aim to democratize advanced, globally distributed database capabilities once available only to large enterprises. By eliminating upfront hardware costs and shifting to consumption-based, pay-as-you-go pricing, serverless databases remove traditional barriers to entry and significantly reduce operational complexity and overall costs.

Organizations, regardless of size, can now employ agentic AI, scaling resources dynamically as workload demands change. This flexibility is vital, as AI usage patterns are unpredictable, making rapid scaling essential. Serverless distributed databases combined with cloud economics make powerful AI and analytics accessible to a much broader market, even midsized organizations previously unable to justify infrastructure investments.

Always-on in Real Time

Agentic AI systems require always-on access to up-to-date, distributed data, without tolerance for delays or stale information. In mission-critical autonomous scenarios, these systems depend on real-time access to information from all relevant sources. Zero copy, zero ETL architectures are foundational for many use cases, with instant space-efficient cloning of data sets enabling timely and point-in-time reporting, analytics, development, and testing without traditional data movement or duplication.

Combined with real-time replication technologies like Raft-based consensus protocols, these systems deliver continuous operation, automatic failover, and zero data loss across regions. For the next generation of AI, having seamless, resilient, and compliant access to distributed data will be transformative.

Technical Capabilities Enabling Success

Modern distributed database platforms provide additional comprehensive capabilities essential for agentic AI success. For example, the hyperscale AI vector indexes needed to perform similarity search on enterprise-wide data can be sharded and loaded into memory across many distributed nodes to speed up individual searches and enable greater multi-query throughput. Organizations can also combine similarity search with business data in single distributed queries to generate a more comprehensive understanding of their customers and operations using Retrieval Augmented Generation (RAG).

Near-instantaneous elasticity enables compute and storage capacity to scale dynamically online without data movement or downtime to efficiently meet the demands of variable AI workloads.

Oracle Tackles the Challenges

To address the challenges of agentic AI, Oracle announced on Thursday, August 7, the launch of Oracle Globally Distributed Exadata Database on Exascale Infrastructure. According to the company, the serverless cloud service combines Oracle's proven distributed database capabilities with easy-to-use cloud automation and Exascale's independently scalable, hyper-elastic compute and storage architecture to help enable always-on, auto-scaling performance through a pay-as-you-go model with no upfront hardware costs.

The solution offers the following capabilities for agentic AI deployments: automated data distribution that helps keep country-specific data within required regions for data residency; dynamic, near-instantaneous elastic compute capacity without data movement to optimize the performance of variable AI workloads; Raft-based replication designed to quickly deliver automatic failover with zero data loss across locations for always-on agentic AI; support for hyperscale AI vector search and retrieval augmented generation (RAG); and Exascale's serverless architecture to reduce cost and provide elasticity for agentic AI in any size organization.

Oracle states that customers are already developing solutions using Oracle's Globally Distributed Database capabilities, including mobile messaging, credit card fraud detection, payment processing, personalized marketing, and smart meters.

The Future of Autonomous Intelligence

According to IDC's Future Enterprise Resiliency & Spending Survey, Wave 1 (February 2025), organizations prioritizing AI strategy focus on responsible AI, ethics, and data management, emphasizing frameworks for ethical AI use and high-quality, well-governed data. As agentic AI systems become more sophisticated, the ability to access distributed data while maintaining sovereignty compliance will become a competitive requirement. Organizations that can effectively deploy autonomous AI systems globally while respecting local regulations will have significant advantages in customer experience, operational efficiency, and market responsiveness. They will also better maintain compliance and control costs.

Modern distributed database platforms, such as Oracle Globally Distributed Exadata Database on Exascale Infrastructure, provide the essential foundation for this success, enabling agentic AI systems to operate at scale across regions while turning data sovereignty from a compliance challenge into a strategic business advantage.

Message from the Sponsor

Oracle Globally Distributed Exadata Database on Exascale Infrastructure is available in Oracle Cloud Infrastructure (OCI) regions around the world to support mission-critical and agentic AI applications. Its automated data distribution policies and variety of replication methods can be used to help organizations meet data residency requirements and deliver always-on applications, extreme performance, and scale. Please visit Oracle Globally Distributed Databaseto learn more, see LiveLabs and demos, and access additional documentation.