Agentic AI is not just a model problem. It is exposing gaps in how teams store, share, retrieve, and coordinate context across applications, agents, and people.

In this episode, Amir talks with Karthik Ranganathan, cofounder and co CEO at Yugabyte, about why databases are under new pressure as AI moves from model serving into agentic workflows. They discuss Yugabyte’s evolution, the limits of today’s data infrastructure, and why memory, knowledge, and shared context may become central to how agentic systems actually work.

Practical takeaways

• Agentic workloads push databases beyond simple relational access because agents may need relational, vector, graph, NoSQL, scale, and multi tenant support in the same workflow.

• A query can be optimized inside each data store and still be slow, expensive, or wasteful when the work spans multiple systems.

• Context sounds simple to humans, but it becomes messy when it includes private memory, shared project knowledge, conversation history, team collaboration, and agent actions.

• Human handoffs can erase much of the speed promised by agents when teams have to copy outputs, re explain reasoning, and manually reconcile conflicts.

• Yugabyte is working on Meko as a data infrastructure layer for agents, with a focus on memory, knowledge, context quality, and shared learnings.

Timestamped highlights