Unification for AI

While the success rate is improving, most enterprise AI projects still fail. Though not always the culprit, data -- or problems with it such as disorganized data and poor data quality -- is often one of the main reasons.

The story here is consolidation, not any single feature. Sprawl is the problem, and bringing relational, analytical, vector and agentic work onto one governed Postgres foundation is a real answer to it.

In response, many data management and analytics vendors have introduced capabilities this year aimed at better enabling customers to organize, discover and connect agents with the data and business logic that give them the situational awareness to perform as intended. For example, in June alone AWS unveiled a context layer for AI, Databricks similarly revealed a context layer for agents as part of its Genie line of capabilities, Microsoft added features that turn its fabric platform into a foundation for AI, and Snowflake launched tools to help govern and standardize context for agents.

Now, EnterpriseDB is similarly adding capabilities that aid AI development in a move motivated, in part, by customer feedback, according to Max Romanenko, the vendor's chief engineering officer.

"Customer feedback was consistent across the board -- teams were spending a disproportionate amount of engineering time on data movement and database management that shouldn't demand constant manual intervention," he said.

In addition, market observations played a role in EnterpriseDB's development of Converged Analytics and Agentic Database, Romanenko continued, noting that Databricks and Snowflake each acquired PostgreSQL database vendors in 2025 to add data layers to their platforms.

"We've been that operational layer for 20 years, so the question for us was how to surface what we already had so it could serve transactional, analytical and now agentic workloads as one unified system," he said. 

To join the operational data that informs day-to-day operations and analytical data that helps determine long-term strategies, Converged Analytics publishes operational data to an Apache Iceberg source where it can be made available to real-time and analytical engines and queried through a unified, governed PostgreSQL interface.

Results include substantially faster query speeds, data migration efforts that are reduced to hours rather than weeks or months, and lower cost of ownership based on a per-core pricing model where customers are charged based on how many CPU cores they use -- which fixes the cost -- rather than fluctuating usage.

Like Converged Analytics, Agentic Database combines disparate data types. But beyond its SQL interface for relational, JSON, time-series, geospatial and vector data, it is designed to improve the operation of EnterpriseDB's database platform with agents that proactively discover and resolve issues within organizational guidelines. Each customer can set guardrails, including row-level and role-based access controls, that agents then execute to dramatically reduce manual work.

Beyond Converged Analytics and Agentic Database, EnterpriseDB's update includes governance at the data layer that combines vector search, structured data and analytics, a bring-your-own-cloud (BYOC) option that allows customers to apply AI to their data where it resides, and EDB Developer Cloud to provide a collaborative environment for AI development.

Converged Analytics and Agentic Database are generally available, while BYOC and EDB Developer Cloud are in preview and expected to be GA during the second half of 2026.

Aslett noted that while Converged Analytics and Agentic Database are valuable additions -- one fueling both real-time analytics and historical reporting and the other automating database administration and data management tasks -- they also help EnterpriseDB distinguish itself in a competitive market.

"While many software vendors provide PostgreSQL distributions and database-as-a-service offerings, EnterpriseDB is differentiated by the level of its expertise as well as distinctive features," he said. "These have traditionally included Oracle compatibility, and more recently advanced support for the development and deployment of AI applications and agents, both on-premises and in the cloud."

Pratt called Converged Analytics the most significant of the new features.

"Killing the ETL between operational and analytical data is exactly the convergence enterprises tell us is now a priority," he said.

Meanwhile, from a competitive standpoint, the new features represent innovation and help distinguish EnterpriseDB from some of its competitors, Pratt continued, noting that Databricks is the vendor providing the most similar capabilities with Lake Transactional/Analytical Processing and Lakebase.

"EnterpriseDB is chasing the same convergence, but on open Postgres that runs on the customer's own infrastructure," he said. "For sovereignty-conscious enterprises, that is hard to match."

Consumer appeal

Just as user feedback helped fuel EntrepriseDB's development of Agentic Database and Converged Analytics, conversations with customers informed the BYOC and governance capabilities now in preview, according to Romanenko.

"Enforcing rules tied to what the agent was actually created to do inside the data layer rather than trying to observe it from outside felt like the right architectural extension of where we already were," he said.

Meanwhile, to appeal to potential new customers, Pratt suggested that EnterpriseDB add more capabilities that could appeal to self-service developers. He noted that EnterpriseDB has a strong base of large enterprise customers, but self-service capabilities could enable it to attract smaller companies that helped vendors such as Neon and Supabase grow.

"A natural next step is bottom-up adoption," Pratt said. "EntrpriseDB is strong with large enterprises, and a self-serve experience for developers … would bring the next generation of users in alongside that base."

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.