Continual learning is one of the hardest and most critical features to build for AI agents in production. Every company has deep institutional knowledge fragmented across docs, teams, and the minds of tenured engineers. Much of it has never been written down. This is the single biggest barrier to useful AI agents in production. We think about it as three types of knowledge: Organizational Vocabulary: naming conventions, shorthand, acronyms. This is the most straightforward to teach. Tool-Specific Knowledge: how to navigate systems, where data lives, how it connects. It's time-consuming to map manually and varies across companies. Decision-Trace Knowledge: the hardest to capture. The intuition that separates system experts from everyone else. Pattern recognition built over years, rarely documented, almost always context-dependent. Across all three, Resolve learns on its own, explicitly and implicitly, but always verifies with the humans who know the system best. Everything it learns is viewable, editable, and version-controlled. Resolve is already becoming the single source of truth for organizational context in production environments. More in my Introducing AI for Prod blog, link in replies.