8 AI Agent Memory Patterns for Production Systems (Beyond Basic RAG)

8 AI Agent Memory Patterns for Production Systems (Beyond Basic RAG) Every AI agent tutorial shows stateless request-response. User asks, agent answers, context vanishes. Real agents need memory. Not just "stuff the last 10 messages into the prompt" — actual structured memory that persists, compresses, and retrieves intelligently. Here are 8 memory patterns we use in production, ranked from simplest to most sophisticated. 1. Sliding Window with Smart Summarization The baseline. Keep recent messages, summarize old ones. But do it properly. # memory/sliding_window.py from dataclasses import dataclass , field from datetime import datetime import json @dataclass class Message : role : str # "user", "assistant", "system", "tool" content : str timestamp : datetime = field ( default_factory = datetime . utcnow ) token_count : int = 0 metadata : dict = field ( default_factory = dict ) class SlidingWindowMemory : """ Maintains a context window with automatic summarization. """ def __init__ ( se