Chase identifies three granularities at which context learning happens: agent-level (the agent updates its own persistent configuration — OpenClaw’s SOUL.md that evolves over time), tenant-level (each user, org, or team gets their own context that updates independently — Hex’s Context Studio, Decagon’s Duet, Sierra’s Explorer), and mixed (an agent can have agent-level + user-level + org-level context updates simultaneously).
This directly extends Two-tier agent memory separates organizational workflow knowledge from individual user preferences, which identified the deployment/user split in Glean’s trace learning system. Chase’s framework adds the agent level as a third tier — the agent itself has persistent configuration that evolves independently of any tenant. Your own setup is a living example: CLAUDE.md skills and memory operate at the “tenant” level (you, Ayush), while Claude Code’s built-in behaviors are the “agent” level, and Anthropic’s model improvements are the “model” level. The implication for Cross-user knowledge transfer works without fine-tuning — just a database and prompt engineering is that transfer happens at the tenant level — one user’s context learnings can benefit others at the org level without touching the model or harness.