The Hidden Cost of Statelessness in Agentic Systems When building AI agents, we often overlook one critical component: memory. Without it, every interaction starts from scratch – no prior knowledge, no user preferences, and no recall of past attempts. For simple tasks, this might be okay, but for complex workflows or repeated conversations, statelessness becomes a hard limit on what your system can achieve. Memory is the key to accumulating context across sessions, personalizing responses over time, avoiding redundant work, and building on prior successes. But implementing it isn't straightforward – agentic systems need multiple types of memory, each with its own requirements. Let's dive into how you can design, implement, and evaluate a robust memory system for your production agents. Memory Types: Short-Term vs Long-Term In most agentic systems, we distinguish between short-term memory (STM) for temporary information and long-term memory (LTM) for permanent storage of learned preferences or knowledge.…