About speaker

Oleksandra Bovkun

Databricks

Sr. Developer Advocate

From architecting global data platforms to fine-tuning massive performance projects, Oleksandra Bovkun knows that the biggest barrier to AI isn’t the tech it’s the data and complexity. As a Developer Advocate at Databricks, Oleksandra uses her background in Applied Mathematics and years of engineering experience to bridge the gap between “it works on my machine” and “it works at scale.” She’s a vocal champion for the data community, dedicated to breaking down the high walls of data engineering and governance so that teams can spend less time fighting their infrastructure and more time building what matters.

Talk details

Oleksandra Bovkun Sr. Developer Advocate at Databricks

Agents AI Architecture Data Databases

The shift from simple chatbots to autonomous agents requires more than just better prompts; it requires Agentic Memory. While standard RAG provides a snapshot of data, true AI-native apps need a persistent, evolving state to reason effectively over time.
In this session, we explore how to build this memory layer using Lakebase. We’ll cover:
– Architectural Shifts: Moving from stateless chats to stateful, goal-oriented agents.
– Memory Management: Leveraging Lakebase and Lakehouse to unify structured data and unstructured context for long-term recall.
– Optimization: Practical strategies for memory condensation and reducing context drift.
Attendees will move beyond chat applications and learn how to build autonomous systems that actually remember.

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