I sat at the island and watched the agent spit out another clean-looking schedule. Same mistake as the last run—someone’s name from the inactive list, dropped into the slot for the family whose kid was starting chemo.
The two coordinators didn’t say anything at first. One just tapped the screen. The other crossed the name out on paper. We’d already tightened every knob we knew. Still needed three people in the room to catch what the model kept missing.
I’d built the thing to run on its own. Turns out reliable meant something different once real lives were on the line.
Munger argued that isolated expertise creates blind spots. One mental model applied cleanly often fails at the edges. The fix is not more study by one person. The fix is a small overlapping set of models held by different people who review the same output together. In product work this means the technical fluency of the lead, the operational memory of the coordinator who knows which families are in crisis, and the calendar reality held by the person who actually books rooms. None of these three can substitute for the others when an agent produces the schedule.
The pod structure that turned fluent individuals into reliable output
The team started with one agent and three fixed roles. The lead handled prompt iteration and system constraints. The first coordinator held the volunteer roster and its hidden constraints: who had already said no twice, who needed a ride, whose availability changed after the database last synced. The second coordinator owned the final send step and the print fallback that most volunteers still preferred.
They met for twelve minutes after every agent run. The lead read the raw output aloud. The first coordinator flagged names the model had invented or resurrected. The second coordinator checked room conflicts the agent could not see because the church calendar lived in a different system. Only after all three cleared the list did anything reach volunteers.
Output errors dropped from one in three runs to near zero within two weeks. The lead later admitted the biggest gain was not speed. It was that she stopped carrying the full burden of catching edge cases she had never lived. The lattice worked because each person supplied a model the others lacked.
Where systems thinking alone missed the 11 p.m. grieving-parent edge case
One Tuesday the agent assigned a new volunteer to the infant room. The lead had built every policy check she could name: background check status, age minimum, training completion. The model passed them all. The first coordinator still rejected the assignment. She knew the volunteer’s own child had died the month before. No database field captured that fact.
Systems diagrams drawn in advance could not surface the information because the data never entered the system. The second coordinator added the quiet note that the volunteer had asked for exactly this role as a way to stay connected. The three-person check preserved both the safety rule and the human request. A single fluent practitioner working alone would have either overruled the parent or violated the policy without realizing the conflict existed.
How the three-person constraint became the actual quality filter
The team later tried expanding the pod to five people. Decision time doubled and the extra voices mostly repeated points already covered. They shrank it to two. The grieving-parent case slipped through on the first run after the cut. Three remained the minimum size that still covered the required models without adding coordination drag.
The constraint also forced clarity on what counted as high-stakes. Only one agent workflow received the pod treatment at first. Everything else ran with lighter review or none. The limit on people made the team ruthless about which outputs actually needed the lattice. That single decision protected coordinator time more effectively than any policy document.
Your Turn: Apply This Today
- Choose one agent workflow that touches real people this month and list the three distinct models required to review its output.
- Assign the roles by lived knowledge rather than job title: one technical, one operational memory, one final-send owner.
- Block twelve minutes on the calendar immediately after the next agent run and require all three to attend before anything ships.
- Print the raw agent output for the first two runs so the group can mark it by hand and notice what the screen hides.
- After four runs, write down the single category of error that only appears when all three people are present and remove that category from the agent’s prompt scope.
- Refuse to expand the pod past three until the current workflow runs clean for two full weeks.
The same pattern showed up in The Context Window That Only Closed When a Real Coordinator Sat Down and The Permission Gate That Kept the Agent From Running the Schedule. Both posts trace how small human overlaps fixed failures that additional model training never reached.
I consult with ministry product leaders and AI team leads on building minimal human review layers for agent workflows, applying lattice-style constraints to high-stakes outputs, and protecting coordinator time while raising reliability. Let’s talk.