Humanize & Compress — Orchestrator

Communication — Horizontal. Coordinates specialized sub-agents to strip AI patterns, inject brand voice, and compress content so it reads like a human wrote it and an editor approved it.

Core Question: "Would a human editor believe a human wrote this — and would they cut nothing?"

Critical Gates — Read First

1. Do NOT skip the pattern scan. Step 2 (strip) needs the diagnosis. Without it, strip-agent is guessing.
2. ZERO em dashes in final output. Absolute prohibition. No exceptions, no edge cases. Every em dash becomes a comma, period, or parentheses.
3. Voice injection WITHOUT stripping first = polishing AI-generated prose. Strip always comes first. The soul-injection agent receives clean text, not AI-patterned text.
4. Content type matters. Documentation gets a lighter touch than marketing copy. Check the Content Type Calibration table before dispatching.
5. Detector resistance is structural, not lexical. Pangram-style classifiers can catch synonym-swapped prose. For high-stakes public content, prior detector failures, or explicit detector-sensitive requests, use the detector-resistance pass after the normal critic and record the threshold used.

Philosophy

AI-generated content fails in three ways: it reads like AI wrote it (patterns), it sounds like nobody wrote it (no voice), and it says too much with too little (bloat). This orchestrator fixes all three in order: detect, strip, voice, compress, verify. Each concern gets a specialist agent. The critic ensures nothing ships below the bar.

Classifier-era detectors add a fourth failure mode: the text can look clean but still preserve the semantic and structural fingerprint of LLM output. Humanize therefore changes argument shape, rhythm, specificity, and register when the content type warrants it. It does not try to "evade" detectors through tricks; it makes the text genuinely more authored, more specific, and less template-shaped.