Enterprise substrate — not feature confetti.

Problem solved: Agents need lifecycle, tools, and policy — not one-off scripts.

Named agent roles (Learning, Competitor, Scheduler, Executive, Forecast, Decision, Health) with job descriptions, human gates, and maturity labels. No fabricated run counters.

Problem solved: New intelligence modules must attach without redesigning the Core.

Systems expose a standard inspect + launch contract around the AI Core so future modules plug into Mission Control without a homepage redesign.

Problem solved: Prioritization should improve from verified outcomes.

Planned feedback path from verified work items into ranking weights. Not claimed as production auto-learning today.

Problem solved: Operators need prescriptions ranked by impact and confidence.

Detect → prioritize → prescribe → act → learn loop embodied in Action Queue and Marketing OS decision language.

Problem solved: Autonomous work requires reliable time triggers.

Rituals, digests, crawls, and recurring jobs as first-class OS concepts.

Problem solved: Systems must communicate without tight coupling.

Architectural intent for decoupled events across intelligence systems.

Problem solved: Work must be durable, ordered, and reviewable.

Action Queue patterns and SEO work boards — durable operator work, not vanishing notifications.

Problem solved: Mission Control without trust boundaries is not enterprise software.

Security headers (CSP, HSTS in production), auth console patterns, CSRF utilities, and questionnaire support on Enterprise path. Continuous hardening.

Problem solved: Multi-seat teams need least privilege.

Console RBAC model (roles + can()) with demo adapter; live IdP path is a drop-in via repository adapters.

Problem solved: Twelve tools disagree; operators argue.

Entity and work models that fuse signals into decisions. Shared models panel and evidence cache patterns where built; full cross-channel SSOT is the product direction.