
Created multica-ops — an open-source operating model and advisor that builds and runs an autonomous company of AI agents on Multica
- Progressive onboarding interviews stand up the entire workspace — conductor, squads, skills, integrations — in minutes, not days. Small tasks stay three questions.
- A dedicated conductor agent drives the conveyor through stage barriers and parallel review gates without human dispatcher oversight. A failing gate sends work back automatically.
- The conveyor is a closed loop: features are shipped, measured against discovery-defined metrics, and learnings feed back into the roadmap — not a dead end at merge.
- Every release writes a cost/effort ledger — tokens, estimated dollars, wall-clock time, broken down per agent and per human — committed to git. Budgets cap spend in tokens, dollars, or time.
- Session-limit recovery is first-class: detection with reset time, one-command queue recovery, capacity levers, model tiering across runtimes.
- An autonomous role-builder researches best practices, finds or drafts skills, and packages new specialists for any craft — hiring can run autonomously with owner approval.
- Human governance: per-member access control, review checkpoints (who signs off where), and teammate onboarding by email — destructive actions always route to the owner.
- Long-term survival: full-circle health checks (runtimes, integrations, expired tokens), git-backed skill upgrades with rollback, assisted provider switching across the whole team, and two-way drift sync.
- Optional experts and personas squads provide advisory review and user-simulation walkthroughs during Design QA.
- Everything beyond the invariants is opt-in and overridable — stacks, services, autopilots, operating modes — and every choice accepts "other."




























































































































































































