A Practical Guide to Memory for Autonomous LLM Agents

Memory architecture is the most critical differentiator in autonomous LLM agent systems, often more impactful than model selection. Drawing from a formal survey (arxiv 2603.07670), this post maps a taxonomy of four temporal memory scopes (working, episodic, semantic, procedural) and five mechanism families (context-resident compression, RAG stores, reflective self-improvement, hierarchical virtual context, policy-learned management) to real-world agent implementations. The write-manage-read loop is highlighted as the core framework, with 'manage' being the most neglected phase. Key failure modes covered include summarization drift, attention dilution, semantic-causal mismatch, memory blindness, staleness, self-reinforcing errors, and contradiction handling. Practical recommendations include building memory incrementally by temporal scope, keeping raw episodic records, versioning reflective memory, and treating procedural memory files as source-controlled code.