AI Architecture
AI Agent Memory & Session Management: Working, Episodic, and Semantic Memory
Architect multi-layered memory systems for AI agents using Redis session stores, vector embeddings, and persistent knowledge graphs.
The Three Pillars of Agent Memory
1. Working Memory: Short-term conversation history inside the prompt window. 2. Episodic Memory: Past task experiences and action logs stored in Redis. 3. Semantic Memory: Long-term domain facts and user preferences indexed in vector stores.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
2026 Frontier Stack: LangGraph Checkpointing, Mem0 Scoped Memory & Prefix Caching FinOps
Architect enterprise AI agents with decoupled lifecycles: prompt KV-cache optimization (90% savings), LangGraph state persistence, and Mem0 long-term memory.
Model Context Protocol (MCP) Guide: Connecting LLMs to Local Databases and Tools
Learn the open-source Model Context Protocol (MCP) standard created by Anthropic and how it turns LLMs into extensible agents connected to your infrastructure.
Prompt Caching Architecture: Slashing LLM API Costs and Latency by 90% via KV-Cache Reuse
Master Anthropic and Gemini Prompt Caching to slash API bills and reduce latency on long documents, system instructions, and multi-turn chats.