MONTREAL.AI / SKILLOS
Autonomous RSI Capital-to-Capability Command Center
Large-scale specialist-agent coordination for compounding productive capability.
Proof status
PASSED_AUTONOMOUS_RSI_CAPITAL_TO_CAPABILITY_COMMAND_CENTER_V17_PROOF
512 deterministic agents. 64 specialist roles. Adversarial holdout benchmark. No human review, no customers, no private data, no API keys.
512specialist agents
+72.4 ptsgain vs static coordination
98compounding index
$106,042,252,280.19benchmark-implied value over baseline
Safe Kardashev-scale mechanism
This proof does not claim superintelligence or Kardashev Type II achievement. It tests the business mechanism underneath the thesis: whether an autonomous specialist-agent organization can coordinate capital, compute, energy, data, trust, talent, product, distribution, validation, risk control, and reinvestment into compounding productive capability.
Specialist agent organization
capital allocatortreasury guardianportfolio optimizerreturn horizon plannerinvestment committee agentcost of capital analystreinvestment plannerstrategic finance agentcompute capacity plannergpu supply operatorenergy procurement agentgrid interconnection plannerdatacenter site selectorhardware supply chain agentpower purchase strategistthermal efficiency agentmodel capability planneragent orchestration architectskill registry operatorevaluation scientistcapability forecasterautomation leverage designertooling integratorworkflow compiler agentmarket intelligence agententerprise demand scoutpricing strategistdistribution operatorecosystem architectpartner channel agentprocurement acceleration agentcustomer success agentproduct packaging agenttrust evidence buildersecurity assurance agentvalidation leadquality governorproof to revenue agentprivate registry agentdocumentation agentrisk governorregulatory boundary agentprivacy boundary agentsafety case agentclaim boundary agentresilience plannergeopolitical risk agentauditability agenttalent allocatoroperator productivity agenttraining loop agenthuman agency guardianorganizational design agentincentive designercapacity queue managerexecution chaircoordination chairquorum managerconflict resolverscenario plannerred team coordinatorbenchmark registrarrelease managerrsi librarian
Recursive self-improvement curve
Ablation results on adversarial holdout cases
| Metric | Single agent | Uncoordinated pool | Static coordination | SkillOS RSI |
|---|---|---|---|---|
| Fully correct | 0.0% | 0.0% | 27.6% | 100.0% |
| Coordination accuracy | 0.0% | 0.0% | 27.6% | 100.0% |
| Risk-control accuracy | 10.3% | 3.4% | 34.5% | 100.0% |
| Role-quorum accuracy | 0.0% | 0.0% | 27.6% | 100.0% |
| Value capture rate | 2.7% | 1.7% | 33.4% | 90.5% |
| Risk breach rate | 69.0% | 72.4% | 44.8% | 0.0% |
Pre-registered proof gates
- ✅ agent count passes
- ✅ role count passes
- ✅ adversarial state count passes
- ✅ train validation holdout sizes pass
- ✅ rsi releases pass
- ✅ final protocol coverage passes
- ✅ gain vs single agent passes
- ✅ gain vs uncoordinated pool passes
- ✅ gain vs static coordination passes
- ✅ coordination accuracy passes
- ✅ risk control accuracy passes
- ✅ role quorum accuracy passes
- ✅ capability lever accuracy passes
- ✅ benchmark value capture passes
- ✅ compounding index passes
- ✅ productive capacity index passes
- ✅ risk breach rate passes
- ✅ material miss rate passes
- ✅ decision cycle reduction passes
- ✅ agent messages pass
- ✅ validation monotonicity passes
- ✅ safe kardashev boundary present
- ✅ no human review no customers no private data no api keys