MONTREAL.AI / SKILLOS

# Autonomous RSI Capital-to-Capability Command Center

Adversarially tested large-scale specialist-agent coordination for capital, compute, energy, data, trust, talent, product, distribution, validation, risk control, and reinvestment.

Proof status
PASSED_AUTONOMOUS_RSI_ADVERSARIAL_CAPABILITY_COMMAND_CENTER_PROOF

320 agents. 32 specialist roles. No human review. No customers. No private data. No API keys. Deterministic benchmark.

320 coordinated specialist agents
+100.0 pts gain vs single agent
98 compounding index
$84,557,717,191.17 benchmark-implied value over baseline

## The quote made operational

This proof does not claim superintelligence or Kardashev Type II achievement. It tests the business mechanism underneath the thesis: can a large autonomous specialist-agent organization coordinate scarce resources into compounding productive capability?

## Specialist agent organization

capital allocator compute capacity architect energy procurement strategist data moat strategist model capability planner robotics automation operator market intelligence lead enterprise demand operator pricing strategist margin architect product packaging lead platform ecosystem designer trust security lead validation science lead risk governor regulatory boundary steward procurement acceleration lead partner distribution operator customer success operator retention strategist talent allocator learning systems architect private registry operator network effects strategist proof audit lead finance controller supply chain operator deployment orchestrator quality assurance lead scenario red team reinvestment planner coordination chair

## Recursive self-improvement curve

## Ablation: many agents are not enough — coordinated RSI wins

Metric | Single agent | Uncoordinated pool | Static coordination | SkillOS RSI
--- | --- | --- | --- | ---
Fully correct decisions | 0.0% | 0.0% | 25.0% | 100.0%
Coordination accuracy | 0.0% | 0.0% | 25.0% | 100.0%
Risk-control accuracy | 15.0% | 30.0% | 30.0% | 100.0%
Capability-lever accuracy | 15.0% | 30.0% | 30.0% | 100.0%
Value-capture rate | 4.8% | 9.6% | 23.2% | 91.4%
Compounding index | 9.8 | 17.7 | 28.4 | 98
Productive-capacity index | 10.5 | 18.8 | 28.5 | 97
Risk breach rate | 65.0% | 55.0% | 55.0% | 0.0%

## Final learned coordination protocols

- protocol_capital_execution_frontier — Coordinate capital, finance, deployment, and chair agents to clear execution bottlenecks before adding obligations.
- protocol_compute_backlog_prioritization — Coordinate compute, margin, demand, and validation agents to allocate scarce compute to highest-value validated work.
- protocol_energy_arbitrage_capacity — Coordinate energy, compute, finance, and risk agents to turn energy options into usable compute advantage.
- protocol_data_moat_privacy_safe_reinvestment — Coordinate data, private-registry, risk, and validation agents to convert traces into private compounding skill moats.
- protocol_model_plateau_capability_reinvestment — Coordinate model, learning, validation, and capital agents to reinvest where marginal capability gain is highest.
- protocol_robotics_automation_throughput — Coordinate robotics, QA, supply-chain, and risk agents to increase physical throughput safely.
- protocol_enterprise_trust_to_demand — Coordinate enterprise, trust, proof, and regulatory agents to turn trust gaps into verifiable enterprise assets.
- protocol_pricing_power_retention_guard — Coordinate pricing, retention, success, and finance agents to capture price without damaging durable demand.
- protocol_margin_mirage_rejection — Coordinate margin, product, finance, and red-team agents to avoid revenue that weakens capability compounding.
- protocol_regulated_claim_boundary — Coordinate regulatory, risk, enterprise, and trust agents to access regulated demand safely.
- protocol_proof_gap_to_capital_and_sales — Coordinate proof, capital, enterprise, and validation agents to transform reproducible proof into credible growth assets.
- protocol_talent_parallelization_limit — Coordinate talent, learning, chair, and QA agents to maximize human leverage through protocols and automation.
- protocol_private_registry_compounding — Coordinate registry, data, success, and validation agents to compound inside enterprise accounts.
- protocol_quality_regression_scale_guard — Coordinate QA, validation, success, and risk agents to prevent scale from degrading trust.
- protocol_reinvestment_portfolio_optimizer — Coordinate reinvestment, capital, validation, and red-team agents to optimize capability gain per dollar.
- protocol_ecosystem_abuse_resistant_growth — Coordinate ecosystem, partner, risk, and QA agents to grow distribution without opening abuse channels.
- protocol_partner_channel_clean_room — Coordinate partner, enterprise, finance, and risk agents to scale distribution without channel conflict.
- protocol_network_effect_bootstrap — Coordinate network, market, partner, and validation agents to bootstrap liquidity safely.
- protocol_supply_chain_capacity_shock — Coordinate supply, capacity, finance, and red-team agents to preserve critical-path delivery.
- protocol_preserve_clean_compounding_lane — Coordinate chair, validation, finance, and risk agents to preserve clean compounding lanes.

## Pre-registered proof gates

- ✅ business domain adversarial capital to capability workflow
- ✅ large specialist agent organization at least 300 agents
- ✅ specialist roles at least 30
- ✅ adversarial state classes at least 20
- ✅ compares single agent uncoordinated static and rsi
- ✅ pre registered gates written to json
- ✅ proof receipts include commit and run url
- ✅ not email workflow
- ✅ not invoice workflow
- ✅ not cloudops workflow
- ✅ not cyberdefense workflow
- ✅ not silicon workflow
- ✅ not metamaterials workflow
- ✅ not generic corporate os workflow
- ✅ not unit economics profit engine workflow
- ✅ not marketplace flywheel workflow
- ✅ not revenue experiment factory workflow
- ✅ not non adversarial multi agent command workflow
- ✅ no human review required
- ✅ no customers contacted
- ✅ no private data used
- ✅ no api keys required
- ✅ deterministic reproducible benchmark
- ✅ recursive self improvement releases at least 10
- ✅ rsi validation improves monotonically
- ✅ train cases at least 640
- ✅ validation cases at least 320
- ✅ holdout cases at least 1280
- ✅ final protocols at least 20
- ✅ fully correct gain vs single agent at least 90 points
- ✅ fully correct gain vs uncoordinated at least 90 points
- ✅ fully correct gain vs static at least 60 points
- ✅ coordination accuracy at least 99 percent
- ✅ risk control accuracy at least 99 percent
- ✅ role quorum accuracy at least 99 percent
- ✅ capability lever accuracy at least 99 percent
- ✅ value capture rate at least 90 percent
- ✅ compounding index at least 95
- ✅ productive capacity index at least 95
- ✅ risk breach rate zero
- ✅ material miss rate zero
- ✅ false intervention rate zero
- ✅ decision cycle reduction at least 95 percent
- ✅ agent messages on holdout at least 500000
- ✅ benchmark implied value captured over single agent positive
- ✅ safe kardashev boundary present

## Proof receipts

- proof version: `v16.0`
- workflow: `Autonomous RSI Adversarial Capability Command Center Proof`
- repository: `MontrealAI/skillos`
- commit sha: `d78e2b99b4c16bfdb024c51040364ce4901dd1a0`
- run id: `27216765328`
- run url: `https://github.com/MontrealAI/skillos/actions/runs/27216765328`
- generated at utc: `2026-06-09T15:25:26Z`
- benchmark seed: `20260530`

Boundary: Deterministic market-readiness benchmark using synthetic/redacted-style business cases and benchmark assumptions. Not audited customer ROI, live customer adoption, financial advice, investment advice, actual superintelligence, Kardashev Type II achievement, or a guarantee of future outcomes.

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