Stateful Agents: Building Production-Grade Memory with MongoDB and Voyage AI

Most LLM applications are stateless, leading to 95% of AI pilots failing because they do not learn or adapt. The core barrier to scaling GenAI systems is learning, as most do not retain feedback, adapt to context, or improve over time. Moving from RAG (Retrieval-Augmented Generation) to agentic AI requires adding memory and action.

This presentation introduces a multi-agent architecture, Flow Companion, an AI that learns and teaches. We will demonstrate how to build production-grade memory for agents using MongoDB Atlas and Voyage AI, showcasing five types of memory modeled on human cognition: Working, Episodic, Semantic, Procedural, and Shared memory.

You will learn optimization strategies for both the LLM and MongoDB layers, including context engineering (compressing results, streamlining prompts, and prompt caching) and memory engineering (selective generation, adaptive recall, and forgetting with TTL indexes). We will also explore how to use MongoDB Atlas for Hybrid Search (Full-Text and Vector Search) for fast and efficient retrieval.