Most "AI memory" approaches live local in markdown files. Productivity tools already solve the hard parts (typed records, transitions, links, audit trails), but their UIs aren't built for agents. fait is the translation layer: config-driven naming, multi-backend support (Jira + Notion), and resolved-current-state reads so an agent never has to walk a supersession chain by hand. The result: your AI shows its work in tools your team already monitors. Humans stay in the loop naturally, creating work items, editing decisions, validating findings, and every change is visible on the next read. Shared state means no duplicated research across the team, and agents spend tokens on reasoning instead of memory reconstruction.