Key announcements for data & AI teams
Getting value from AI is not just about better models. It depends on whether AI can access trusted data, understand business context, act within governance, and respond fast enough to support real workflows. At Data + AI Summit 2026, Databricks announced several capabilities that move its Data Intelligence Platform further toward that future.
Real-Time Lakehouse
Reyden brings real-time performance directly to the lakehouse
One of the biggest announcements was Lakehouse//RT, powered by Reyden, Databricks’ new engine for real-time analytics and high-concurrency workloads.
This matters because many organizations have historically needed separate serving layers for low-latency dashboards, operational applications, and real-time analytical workloads. Those systems can be fast, but they often introduce duplicate data, separate pipelines, additional cost, and fragmented governance.
Lakehouse//RT is designed to bring millisecond-level performance directly to governed Delta Lake and Apache Iceberg tables. For data teams, the value is not just faster queries. It is the ability to simplify architecture, reduce unnecessary data movement, and support operational analytics on the same governed foundation already used for BI, AI, and data engineering.
Agent Engineering
Omnigent introduces a new layer for building and controlling agents
Databricks also introduced Omnigent, an open-source meta-harness for working with AI agents.
As organizations experiment with agents, they often end up using multiple tools, models, frameworks, and coding assistants. That can create a fragmented experience where context, policies, sessions, and collaboration do not carry across environments.
Omnigent is designed to sit above individual agent harnesses and make them easier to compose, control, and share. It gives teams a common layer for running agents across different tools, applying policies, collaborating on live sessions, and keeping work portable as the agent ecosystem continues to change.
For engineering teams, this is an important shift. The future of AI development will not be one agent in one tool. It will be teams of agents, tools, models, and humans working together in controlled workflows. Omnigent gives that emerging pattern a more structured foundation.
Enterprise Context
Genie Ontology gives AI the business context it needs
Databricks also announced Genie Ontology, a living context layer that helps AI understand how a business actually works.
This is one of the most important themes across the Summit announcements. AI agents need more than access to tables. They need to understand metrics, definitions, relationships, dashboards, queries, pipelines, documents, applications, and the context behind how teams use data.
Genie Ontology is designed to automatically extract and organize that context into a living graph of the business. Instead of relying only on manually curated documentation or one-off semantic models, it learns from the way data is used across the organization.
That matters because a lot of enterprise AI failure is really a context problem. When AI does not know which metric to trust, which table is authoritative, or how teams define business terms, it guesses. Genie Ontology is Databricks’ answer to grounding AI in trusted enterprise context.
Open Governance
Unity Catalog takes a major step toward true openness
Unity Catalog also received several important updates, but one stands out: external access to managed Delta tables.
This is a meaningful step because one of the knocks on Databricks’ openness has been that managed tables were deeply tied to the Databricks platform. Even though Databricks has long supported open formats like Delta Lake and Apache Iceberg, many customers still wanted more flexibility for external engines to interact with governed, managed data.
With external access to managed Delta tables, engines like Spark and Flink can create and write to Unity Catalog managed Delta tables. That is a major improvement for organizations that want the benefits of managed governance without feeling locked into one execution environment.
It reinforces the direction Databricks is pushing Unity Catalog: one governance layer across data, AI assets, models, agents, tools, and open formats.
What this means for Inviso clients
For the clients we work with at Inviso, these announcements are not just product updates. They reflect where modern data platforms are headed.
AI-ready platforms need four things to come together: performance, context, governance, and openness. Reyden and Lakehouse//RT address the need for real-time performance without unnecessary data movement. Genie Ontology addresses the context problem that often limits enterprise AI. Omnigent gives engineering teams a more practical way to build and govern agentic systems. Unity Catalog’s external access improvements help reduce concerns around lock-in and strengthen the case for open, governed data architectures.
The direction is clear. The data platform is no longer just a place to store and process data. It is becoming the operating layer for analytics, applications, and AI agents.
If you are thinking about what these Databricks updates mean for your organization, we would love to connect.
