OpenClaw and AWS AgentCore systems reveal memory architecture matters more than model choice for autonomous LLM agents. A recent arXiv paper formalizes memory as a write-manage-read loop, where poor management leads to noise and degraded decisions. Working memory (context window), episodic memory (daily logs), semantic memory (curated facts), and procedural memory (behavioral rules) form distinct tiers. Claude Code and similar tools struggle without explicit memory management, often requiring thread restarts for context overflow. MemGPT's hierarchical virtual context and Reflexion-style self-improvement mechanisms highlight ongoing challenges in scaling agent interactions.