Agent Memory Explained: Four Types Every AI Developer Should Know

AI agent memory is the mechanism that allows agents to retain information — such as facts, preferences, and past actions — across sessions and tool handoffs, rather than resetting with each new conversation. Developer Anthony Conti of Astra AI outlines four distinct memory types: working memory (context window), long-term semantic memory (external storage), episodic memory (timestamped event logs), and procedural memory (learned behavioral patterns). As agents increasingly handle multi-day tasks in 2026, the limitations of even million-token context windows have made persistent external memory a critical engineering concern. Most practical systems require only working memory combined with long-term semantic memory, while episodic and procedural types become essential for complex, extended workflows. Conti also clarifies that retrieval-augmented generation is a retrieval technique, not a memory system itself — agent memory additionally governs what information gets written and when.