Data + AI World Tour Virtual Asia Pacific

Building agents is easy; operationalizing them is hard. In this session you will see how Databricks unifies Agent Bricks, the Unity AI Gateway, and Omnigent to architect, govern, and scale intelligent agents. We'll integrate custom MCP servers, implement intelligent model routing, and enforce enterprise controls like PII blocking and token-cost management, with MLflow providing continuous traceability and quality assurance. Learn to move from static experiments to reliable, production-ready agents that are observable, controllable, and governed across your enterprise.

Data leaders frequently face a challenging architectural decision: when deploying customer-facing dashboards or embedding analytics into applications, they often spin up a separate real-time serving layer to handle low latency, high concurrency workloads. While this can address performance, it introduces significant complexity, governance risks, and infrastructure overhead. This session challenges the necessity of that serving layer. We will explore how organizations can consolidate their infrastructure by serving high-concurrency end-user analytics directly from the lakehouse with Lakehouse//RT, through real-world customer case studies and benchmarks.

Every team wants to accelerate work with AI agents, so why are engineers the only ones with true AI coworkers? This session introduces Genie One, the data-smart AI coworker for business users, grounded in your business context. Learn how Genie connects enterprise knowledge and unstructured content to deliver richer insights, orchestrates Genie Agents to complete complex tasks, and takes governed actions on your behalf, including automating recurring work. You will leave with a clear understanding of how Genie connects people, data, and context to get work done.

Your team is under pressure to ship apps and AI agents faster, but the data architecture underneath keeps getting in the way. You're managing a separate operational database and analytics warehouse, maintaining pipelines to move data between them, and still waiting on stale data and untangling governance gaps. This session looks at how teams are simplifying that picture with Lakebase and LTAP. We'll start with the everyday problems: environments that are slow to spin up, databases that are hard to scale, and analytics that can't keep up with what your app just wrote. Then we'll show how a serverless Postgres built on open lake storage changes the day-to-day, from branching a full database in seconds to running analytics on live operational data without building a single pipeline. You'll leave with a clear picture of where Lakebase and LTAP can remove friction from your own stack and how other teams are already putting it to work. Built for data engineers, application developers, and platform teams.

As organizations deploy more AI agents across teams and applications, they face new challenges around agent sprawl, rising AI costs, security, and governance. Unity Gateway provides a unified governance layer to secure AI interactions, enforce runtime policies, manage spend, and monitor activity across models, agents, MCP servers, and enterprise tools. In this session, learn how Databricks, Unity Catalog, and Unity Gateway helps organizations confidently govern and scale enterprise AI with centralized controls, end-to-end observability, and an open ecosystem.

Agents promise to scale instant access to data insights across organizations. However, most data warehouses were built for humans reading dashboards. Agents expose every assumption that breaks. Flawed semantics lead to hallucination, fragmented governance exposes improper data access, and cost/performance issues explode when one analyst becomes a thousand agents. In this session we'll walk through what agentic analytics looks like in practice on Databricks Lakehouse, demoing the patterns customers are running in production today. Following these patterns, you can dramatically increase your data team's impact across your organization while reducing time-to-insight. You'll leave with a clear model of approaches to agentic analytics at scale, and see why Databricks Lakehouse is built for it.

Databricks is setting a new standard for BI—combining unified semantics with AI-powered agents to truly deliver on the promise of Agentic Business Intelligence. Whether through natural language with Genie, AI/BI Dashboards, or a combination of both, users can move seamlessly between conversational and visual analysis in a single, integrated experience. In this session, we’ll cover:
- An end-to-end walkthrough of agentic BI on Databricks
- The latest innovations and customizations capabilities to simplify access for business users
- How unified semantics power consistent, trusted answers across Genie and AI/BI Dashboards
- How agents, Genie, and dashboards work together to accelerate analysis
- How conversational and visual workflows come together to accelerate insights and actions

AI is only as good as the data that fuels it. To build and scale models, organizations need a way to move past fragmented data silos and complex ETL. OpenSharing is the next evolution of Delta Sharing, the most widely adopted open protocol for secure zero-copy data sharing. We’ll dive deep into:
- Fueling AI With Openness: How to share live data and AI assets with any recipient—regardless of their platform—to accelerate model training and deployment.
- Latest Innovations: A first look at enhanced governance, automated provider workflows and expanded support for complex assets like AI assets and unstructured data.
- Real-World Scale: Real-world use cases showing how organizations use the protocol to unify their data ecosystems and manage AI assets at scale.
- From live demos to architectural best practices, you’ll walk away with a roadmap to unify your data ecosystem and lead the next wave of open collaboration.

Cyberattacks are no longer just human-operated. AI-driven threats now move at machine speed, exploiting gaps across systems while your SOC is still triaging yesterday's alerts. Traditional SIEMs weren’t built for this reality: they force painful trade-offs between data volume, retention and cost, leaving security teams flying blind with only a fraction of their telemetry in view. In this session, we’ll take you inside Lakewatch, the Databricks agentic SIEM built on the security lakehouse to deliver complete telemetry visibility and autonomous threat response at machine speed.

Marketing is entering a new era where static campaigns, fragmented context, and manual workflows can no longer keep up with AI-driven customer engagement. In this session, learn how CustomerLake, the new Agentic CDP embedded in Databricks, brings Customer 360, identity resolution, segmentation, activation, and personalization directly to the governed data and AI foundation your business already uses. We’ll explore how Profile Agents and Campaign Agents help data & marketing teams build golden customer profiles, always-on Infinity Campaigns, and truly 1:1 experiences at enterprise scale.