2 min lesson
Name the era to locate the org
Use two rows in "Name the era to locate the org" to state the practical decision rules.
Step 1 of 2
Name the era to locate the orgthe field team's vocabulary, borrowed
The seat-based curve above is your account diagnostic. Cursor's own leadership pairs it with a capability diagnostic: the three eras of AI coding, each defined by where developers spend their attention. Naming the era an org is stuck in is a sharper way to say "here's why depth is shallow" - and it gives a leader the vocabulary to see the next step.
- Era
- Era 1: Tab / autocomplete
- Where attention goes
- Keystrokes
- Tell-tale in an account
- Usage is Tab-only; engineers treat Cursor as faster typing
- Era
- Era 2: Synchronous agents
- Where attention goes
- Steering (riding shotgun)
- Tell-tale in an account
- Agent used live and watched step by step
- Era
- Era 3: Async cloud agents
- Where attention goes
- Reviewing / managing the outcome
- Tell-tale in an account
- Agents run on their own VMs and return artifacts; humans review, not type
| Era | Where attention goes | Tell-tale in an account |
|---|---|---|
| Era 1: Tab / autocomplete | Keystrokes | Usage is Tab-only; engineers treat Cursor as faster typing |
| Era 2: Synchronous agents | Steering (riding shotgun) | Agent used live and watched step by step |
| Era 3: Async cloud agents | Reviewing / managing the outcome | Agents run on their own VMs and return artifacts; humans review, not type |
The Agents Window supports this review-first work by keeping agent outcomes and their evidence together.
A complementary ladder starts at manual coding and ends with AI as a co-founder or builder that works backwards from a desired end state. The eras table above covers the middle of that climb; the two ends are what tell a leader how far their org still has to travel.
The aspirational end state: humans manage agents while agents passively watch over the codebase to keep it safe - automations are the next step toward that.
As agents make it cheaper to produce changes, human review can become the constraint. An org that only counts more generated code is measuring the wrong end of the workflow.
The target behavior is evidence-led review: use a tight summary to locate the decisions and risky changes, inspect the relevant diff and checks, and keep expert judgment at the approval point. Flag this early because deeper adoption needs review capacity as well as generation capacity.