Vector-Databases

Abstract cover illustration showing the AI agent memory loop: write, store, manage, and retrieve across short-term and long-term memory layers

How Memory Works in AI Agents: Turning Stateless LLMs into Persistent, Learning Systems

LLMs are stateless by design. Memory is the external infrastructure that turns them into agents that learn from experience, remember preferences, and actually improve over time. Here’s how it works — technically.

Abstract cover showing the RAG pipeline stages: offline indexing with vector and graph pipelines, and online querying with hybrid search, re-ranking, and grounded LLM generation.

A Deep Dive into Retrieval-Augmented Generation (RAG)

RAG fixes the core problems with pure LLMs — hallucination, stale knowledge, private data — by making retrieval a first-class citizen. Here’s the full technical picture: vector search, hybrid retrieval, GraphRAG, agentic patterns, and what a production stack actually looks like in 2026.

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