Agents absorb execution.
Judgment is what's left — and what multiplies.
AI agents are taking over the execution layer of knowledge work: assembly, reconciliation, drafting, analysis. The empirical finding behind this framework is that domain judgment, not execution skill, is the multiplier on agent labor. The Judgment Coverage Canvas is a repeatable instrument for large organizations: it splits any process into what humans judge and what agents execute, checks that every judgment the process consumes has a named human carrier, and turns the gaps into staffing, control, and delegation decisions.
One process per run · nine canvas blocks · a task-level actor grid (JDEA) · a lifecycle coverage map · nine failure modes wired to detection signals · an eight-step implementation sequence.
The model, explained
The theory in ten minutes: why judgment is the unit that matters, the five judgment types, the JDEA actor split, the bug and twist registries, and the implementation sequence.
MODEL EXPLAINED →See a worked example
Browse the full instrument loaded with a worked quarterly-budgeting redesign — canvas, JDEA grid, live diagnostics, generated report. Read-only sandbox; leave an email to open it.
Run it on your process
Log in to the platform to create workspaces, run the canvas on your own processes with all functional seats present, and export the redesign report.
LOG IN →What a run produces
Judgment coverage matrix
Every judgment type the process consumes — taste, trade-off, subtraction, market, risk — with a named human carrier, or a flagged gap. Headcount follows judgment gaps, not workload.
Delegation backlog
Tasks where one person both judges and executes today — candidates for agent delegation under rules that person owns, freeing the judgment seat.
Control plane + report
Six audit checks with named owners, a bug scan against nine known failure modes, and a circulate-able process redesign report generated from the workshop state.