sessionTrack B

Memory Architectures: How Agents Learn & Remember

Day 1
09:30-10:30
Track B

About This Session

Deep dive into the technical architectures that enable AI agents to maintain context, learn from interactions, and build long-term memory.

One of the most critical capabilities for truly autonomous AI agents is the ability to remember past interactions, learn from experience, and maintain context over long periods. This technical session explores the state-of-the-art in agent memory systems.

We'll cover vector databases, episodic memory systems, semantic memory structures, and hybrid approaches. The session includes practical implementation patterns, performance considerations, and how to balance memory persistence with privacy requirements.

Speakers

ST

Speaker TBA

Role to be confirmed

Speaker details will be announced closer to the conference date.

Learning Objectives

  • Understand different types of agent memory systems
  • Learn practical implementation patterns
  • Master performance optimization techniques
  • Balance memory capabilities with privacy requirements

Who Should Attend

AI EngineersML ResearchersTechnical ArchitectsBackend Developers

Prerequisites

  • Strong programming background
  • Understanding of vector databases
  • Familiarity with LLMs

Don't Miss This Session

Register now to secure your spot at Agentica 2026 and get access to this session and all conference content.