The Critical Gap in Agent Memory: Why Recall Without Learning Fails

AI agents in production typically store facts but cannot learn from past outcomes—a fundamental design flaw that emerges after days of real-world usage. While retrieval-augmented generation excels at information lookup, the absence of experiential memory causes agents to repeat failed approaches without understanding why. This gap exists upstream of search systems: in how agent memory architectures fundamentally fail to distinguish between what happened and whether it worked.