Published on June 2, 2026 by Mike Taylor, Laura Entis and Claude for Every, this guide proposes an 8-level maturity scale for AI adoption, structured around a single axis: at each tier, « you delegate more of your work to—and place more trust in—the AI ». Its thesis runs counter to the race toward sophistication: « a higher level isn't necessarily better », and « the best way to find value in AI is to use it in a way that fits your work ». This is a matching exercise between one's actual workflow and the right level, not a climb for prestige.

The eight levels: (1) Chatbot (conversation with no context — ChatGPT, Claude, Gemini); (2) Copilot (AI in the workspace with access to the file — Cursor, Claude in Excel); (3) Agent (step-by-step execution with approval — Cowork, Codex); (4) Autopilot (one describes the outcome, review of the final result only; tied to vibe coding — Lovable, Claude Code); (5) Workflows (engineers building harnesses with planning, review, confidence checks; shift toward agentic engineering — Compound engineering, Claude Workflows); (6) Assistant (proactive, always-on agents that monitor and surface information without being prompted; e.g. heartbeat.md every 30 minutes — OpenClaw, Claude Managed Agents); (7) Multi-agent (several long-running agents with distinct roles; « firmly in senior engineering territory » — Codex Goals); (8) Orchestrator (an agent-manager directs a team of sub-agents; « highly experimental » — Gas Town, Symphony/OpenAI).

Expect to put in a similar amount of effort with your agents before you can trust them… at the next level of autonomy

— **Mike Taylor** , every.to

The guide provides decision markers: knowledge workers typically operate between levels 1-4, engineers between 5-8; the right level depends on four criteria (output quality, cost, reliability, stakes of failure); and model progress shifts the "safe" autonomy threshold upward. Each level comes with an explicit transition signal ("move up when iterative review becomes a bottleneck").

Two images anchor the pedagogy: the parallel of intern onboarding (« expect to put in a similar amount of effort with your agents before you can trust them ») and the warning about supervision — « you wouldn't brag that you had eight interns working overnight on a key project, and you hadn't checked their output ». A framework directly reusable to structure an adoption doctrine and position a team, converging with harness engineering, the vibe → agentic engineering shift (Karpathy), and the agent manager doctrine.