TL;DR
Every AI platform collapses communication into a single flat voice profile — but knowledge workers switch between at least six distinct registers daily (casual, professional, leadership, field, publishing, builder), and averaging them produces output that's wrong for every context.
The fix is engrams: mode-specific profiles with tone calibration, vocabulary boundaries, structural patterns, values integration, and — most importantly — an anti-pattern library. Anti-patterns are more distinctive than positive examples.
Agent output should amplify intent, not clone raw voice. A casual voice message delivers intent; the engram-calibrated agent delivers a draft that exceeds real-time output quality for that register.
Automatic mode detection (from a config-backed priority hierarchy: override → recipient → role → channel → intent keywords) eliminates manual mode selection entirely.
