The article argues that current AI agents fail not due to reasoning limitations, but a lack of effective memory. Benchmarks often test stateless reasoning, while real-world agents operate across multiple turns and information sources. Simply adding more context to the prompt is prohibitively expensive and ineffective due to token limits and attention dilution.True agent state is a composite of four layers: working memory for the current session, semantic memory for general knowledge, procedural memory for skills, and source memory for provenance. A production-grade architecture involves four layers of memory: a fast context buffer for immediate interaction, a retrieval index for fast recall of semantic and procedural information, a persistence layer for long-term storage with lifecycle management, and a forgetting mechanism to remove stale data.The agent memory loop begins with user input, followed by querying the retrieval index for relevant memories. Then, context is composed by assembling working memory, retrieved memories, and other relevant information. The LLM is called with this composed context, leading to action execution. Finally, memories are updated, and a response is generated for the user.Key to success is the structured assembly of context, prioritizing the system prompt, current goals, and retrieved facts. A rolling summary compresses historical conversation data to manage context window size. During memory updates, new facts are extracted and persisted, and stale memories are managed through compaction and expiration. The article emphasizes that robust memory systems are crucial for building reliable AI agents that can handle real users and sessions.