In the agentic era, intelligence must move to the data, not the other way around. New capabilities make database tuning up to 10x faster and lower analytics TCO by up to 58%—all governed natively at the data layer.

WILMINGTON, Del., June 23, 2026 /PRNewswire/ — Today EnterpriseDB (EDB), the leading sovereign data and AI company, announced new agentic database and converged analytics capabilities for EDB Postgres® AI (EDB PG AI). The offerings bring intelligence, analytics, and governance together at the data layer, on a single open Postgres foundation that enterprises own and control. Relational, analytical, vector, and agentic workloads operate as one on a foundation that EDB will extend through 2026 in a sovereign AI operating system for the agentic era.

In the agentic era, AI runs on the data layer—or it doesn’t run at all. Agents act on live data continuously, at machine speed, and that breaks the old architecture. Sovereignty is no longer optional. Agents can’t reach into someone else’s cloud for a copy of regulated data. Governance can’t hover above the data; it has to be enforced at the row, in the moment of action. And intelligence has to move to the data, not the reverse. A lakehouse is not the source of truth agents need. Only live data—next to the intelligence acting on it, governed the instant it’s used—delivers what they require.

“The industry spent a decade telling enterprises to move everything into the lake. That’s exactly backwards for agents,” said Kevin Dallas, CEO of EDB. “Agents act in the moment, on live data, under real rules. You don’t get speed, accuracy, or sovereignty by reaching into a cloud for a copy. You get it by bringing the intelligence to the data. That’s what we built. Your AI, your data, your rules, on infrastructure you own.”

Agentic database: A self-optimizing foundation

Meeting that expectation starts with the database itself. EDB PG AI, with its agentic database capability, transforms Postgres from a manually managed system into a self-optimizing one. It continuously monitors more than 200 operational and performance metrics, reasons about what needs to change, and—where enterprise policy allows—applies the change automatically. It tunes, scales, and resolves issues before they become incidents.

Crucially, autonomy never comes at the expense of control. Teams choose, per action, whether the system acts automatically, requires human approval, or defers to a scheduled maintenance window. Every action is captured in a full audit trail.

EDB PG AI’s agentic database capability brings relational, JSON, time-series, geospatial, and vector data together through a single SQL interface. Autonomous operations stay inside the enterprise policy and access controls are enforced at the data layer, while the system handles execution at a scale no team could match by hand. It’s a database that runs itself, on the enterprise’s terms.

By putting agents to work on the database, enterprises optimize and tune up to 10x faster. Work that took an expert DBA 60 to 90 minutes of manual digging now takes minutes, with the system spotting the problem, recommending the exact fix in seconds, and applying it where policy allows.

The same optimizations accelerate application performance by up to 8x for end users. Teams catch the majority of performance issues before they reach production, redirecting their expertise from operational firefighting to new value creation.

“Every other approach asks you to move your data to the intelligence. We did the opposite—we put the intelligence in the database, on infrastructure you own,” said Max Romanenko, chief engineering officer, EDB. “It’s the database that runs itself, on your terms. That’s not a feature you bolt on. It’s the foundation.”

Converged analytics: Real-time to petabyte scale, under enterprise control

EDB PG AI collapses the gap between operational and analytical data with a zero-ETL architecture, making all data continuously available for real-time analytics and petabyte-scale warehousing.

It deploys anywhere and is built on open standards end to end. With infrastructure, formats, and engines all under their control, enterprises can unify the whole data lifecycle on their own terms. Every query and agent works from a shared source of truth, with no proprietary lock-in.

EDB PG AI for ClickHouse, generally available as part of the release, delivers sub-second real-time analytics on event and log data. EDB PG AI for WarehousePG provides petabyte-scale depth for historical analysis and complex reporting. For the heaviest workloads, processing can be offloaded to GPU-accelerated Spark. The result is an answer for every analytical workload—real-time, historical, and AI—on a single open core platform, rather than separate proprietary technology for each.

“The real shift here isn’t just speed or cost—it’s control. Built on open Postgres and running on infrastructure they own, customers aren’t renting their data strategy from a cloud vendor anymore,” said Romanenko.

Compared with legacy warehouses and cloud data platforms, EDB PG AI’s converged analytics capability delivers:

Up to 30x faster single-node query performance (in internal testing), and up to 99x by offloading to GPU-accelerated SparkUp to 52% greater scaling efficiency for high-concurrency workloadsUp to 58% lower total cost of ownership, with predictable per-core pricing

Kyobo Book Centre, one of Korea’s largest booksellers, rearchitected its analytics environment on an on-premises WarehousePG foundation. As a result, the organization projected significant savings in TCO while establishing a sovereign data platform ready for AI and vector-driven services.

“Agents don’t act on copies. They act on the real thing—live, governed, right where it sits, with no separate system to secure and no lake to fall out of sync with,” said Romanenko.

AI-ready retrieval, native to the data layer

Agents are only as good as the data they retrieve—and how fast and accurately they can retrieve it. EDB PG AI brings vector search, structured and unstructured data, and analytics together in a single query layer, so agents get accurate retrieval on data they’re already authorized to access, without a separate vector store to secure and synchronize.

Independent benchmarks by McKnight Consulting Group, testing EDB PG AI against leading platforms across the demands of real-world AI agent workloads, found:

Up to 99.4% lower query latency than Databricks, and 93% lower than MongoDBThe highest accuracy of any platform tested—0.911 Recall@10, 17% above Databricks and 26% above MongoDBNew writes queryable in 12 milliseconds, versus 3.8 seconds for Databricks—99.7% faster for the data freshness agents depend on

The result is the sub-second speed and ACID-guaranteed accuracy autonomous agents demand, without architectural compromise.

NTT East, one of Japan’s leading telecommunications carriers, has adopted EDB PG AI to pursue AI-driven network operations. It applies generative AI agents that can autonomously detect, analyze, and respond to network issues in a private environment, where sensitive operational data stays under the carrier’s control.

Governance built into the data layer, for agents—before they execute

As agents take on more enterprise work, the hardest question isn’t what they can do, it’s how to keep them inside the rules. EDB’s answer is to govern agents at the data layer itself, using native Postgres primitives rather than a separate control plane bolted on top.

Now in preview, EDB PG AI enforces agent access through the database’s own roles and row-level security. An agent’s identity, declared purpose, permissions, and the enterprise’s policies are fused into a single constrained query that Postgres executes natively—so every action an agent takes, down to the row, is held to the same rigor as a human’s, with no bypass path and a full session-level audit. Because enforcement runs where the data lives, there’s no new runtime and no proprietary engine to secure separately.

EDB will build on that foundation through the second half of 2026, extending it into full enterprise-wide agent governance, giving every agent a declared owner and boundary and flagging when an agent’s behavior drifts from its stated purpose. The principle remains constant: autonomous systems held to enterprise policy, enforced at the source.

Built on open foundations, delivered with partners

EDB PG AI is built on open Postgres and open table formats, avoiding the proprietary lock-in and sovereignty trade-offs of vendor-controlled platforms. Because it runs wherever enterprises need it—on-premises, in hybrid environments, or across clouds—organizations keep full control over where their data lives and how it’s governed, rather than surrendering it to a single cloud provider. The platform is supported by a global partner ecosystem that includes Dell, IBM, NVIDIA, Red Hat, and Supermicro.

“IBM Power and EDB Postgres AI are empowering enterprises for the AI-native era by providing a secured, sovereign, and AI-ready infrastructure foundation. Together, we enable a resilient data ecosystem that supports data sovereignty,” said Unnikrishnan Rajagopal, WW director for ISV Ecosystem, GSIs and Alliances, IBM.

“Red Hat Ansible Automation Platform plus EDB Postgres AI delivers automated operations with high availability, enterprise-grade security, and deploy-anywhere flexibility. Ansible Automation Platform’s new automation orchestrator, combined with EDB’s new agentic capabilities, enables organizations to rapidly scale automation and build sovereign infrastructure on their own terms, maintaining complete control over portable, governed data,” said Sathish Balakrishnan, GM Ansible Business Unit, Red Hat.

Availability

Agentic database and converged analytics capabilities, including EDB Postgres AI for ClickHouse, are generally available today as part of EDB PG AI. Governance is available in preview. For more information, visit enterprisedb.com.

About EDB

EDB Postgres® AI (EDB PG AI) is the sovereign data and AI platform for the agentic enterprise. Built on Postgres, the world’s leading open source database, EDB PG AI unifies transactional, analytical, and AI workloads in a single architecture, eliminating the data movement, ETL, and operational fragmentation that slow enterprises down. With governance enforced at the data layer and the flexibility to deploy on-premises, in hybrid environments, or across clouds, enterprises operationalize their data and AI on infrastructure they own and control—reaching production-ready sovereign AI in weeks, not months. As one of the most active contributors to the PostgreSQL project, EDB is deeply invested in the vitality of the global open source community. To learn more, visit www.enterprisedb.com.

EnterpriseDB and EDB are registered trademarks of EnterpriseDB Corporation. Postgres and PostgreSQL are registered trademarks of the PostgreSQL Community Association of Canada and used with their permission. All other trademarks are owned by their respective owners.

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