LANDMARK · 6 MIN · BUILD
Designing human checkpoints
After this landmark, you can place a human checkpoint where it actually catches the failures that matter, instead of wherever is easiest to add.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
A human checkpoint only works if it’s positioned where a person can meaningfully catch a problem; before an irreversible or high-impact action, with enough context to actually evaluate it, and without so much volume that review becomes a rubber stamp. Checkpoints added as an afterthought tend to fail one of these: reviewing after the action already happened, showing too little context to judge correctness, or generating so many low-stakes approvals, a person nominally “approving” one of two hundred near-identical customer emails a day, that people click through without reading.
Designing the checkpoint means asking what specifically could go wrong here, and whether a person at this point can actually catch it.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
Before approving an AI-drafted message or action, check that you’re actually reading it, not just clicking approve out of habit; a checkpoint you rubber-stamp isn’t a checkpoint.
MAKE A DECISION
An AI system drafts 200 customer emails a day, all routed through one person for ‘approval’ before sending. What’s the risk with this design?
CARRY THISFind one ‘human in the loop’ checkpoint in a system you use or work with. Does the reviewer actually have time and context to catch a real problem, or has it become a formality?