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__( self, max_tokens: int = 8