Sanketh Gadadinni

As AI agents evolve from experimental prototypes to real-world production systems, enterprises are realizing that model capability alone is not enough. The true challenge lies in how agents manage, retrieve, and retain information across complex, long-running workflows. Getting this right is often the difference between an impressive demo and a system that consistently delivers value. Building effective agentic AI requires a strong approach to context and memory. Developers must understand how different memory types such as in-context, external, episodic, and semantic work together, and when to use each. Techniques like Retrieval-Augmented Generation (RAG) help bridge knowledge gaps, while thoughtful design ensures agents can retain and use information across sessions. Join us for this Tech Talk where Sanketh and Anshul will break down how context and memory shape intelligent agents. Walk away with a practical framework for building AI agents that remember, reason, and scale reliably.