# SkillOS Autonomous RSI Adversarial Multi-Agent Market Command Center Proof

**Status:** `PASSED_AUTONOMOUS_RSI_ADVERSARIAL_MULTI_AGENT_MARKET_COMMAND_CENTER_PROOF`

## Workflow

Adversarial large-scale agentic coordination for profitable market-capture portfolio selection, resource allocation, risk control, and compounding business strategy.

## Proof receipts

- Repository: `MontrealAI/skillos`
- Commit SHA: `ee50afb0a2057f1622690bb7a915b30e0f0efe2c`
- GitHub Actions run: `https://github.com/MontrealAI/skillos/actions/runs/27358141248`
- Generated at: `2026-06-11T15:30:02Z`
- Benchmark seed: `20260530`

## Why this matters

This is a highly scalable business-domain proof for a large specialist-agent organization. It is not an email example, invoice example, CloudOps example, cyber defense example, silicon example, metamaterials example, generic corporate OS example, unit-economics proof, marketplace-flywheel proof, or revenue-experiment proof.

The system must coordinate 160 specialist agents across 20 roles to diagnose adversarial market-capture states, select interventions, preserve risk controls, and choose the correct coordination protocol.

## What is being compared

1. **Single-agent baseline:** one generalist makes the decision.
2. **Uncoordinated agent pool:** many agents emit isolated judgments without required-role quorum.
3. **Static coordinated system:** coordination exists, but without RSI-learned protocols.
4. **SkillOS coordinated RSI collective:** specialist agents coordinate through validation-gated protocols learned from failures.

The proof is designed to show that the advantage is not merely having many agents. The advantage is coordinated specialist agents plus recursive protocol improvement.

## Recursive Self-Improvement

SkillOS runs recursive self-improvement:

training failures → adversarial coordination lessons → candidate multi-agent protocols → validation → released protocol versions → holdout proof

## Holdout ablation results

| Metric | Single agent | Uncoordinated pool | Static coordinated | SkillOS coordinated RSI |
|---|---:|---:|---:|---:|
| Fully correct decisions | 0.0% | 0.0% | 25.0% | 100.0% |
| Adversarial fully correct | 0.0% | 0.0% | 26.7% | 100.0% |
| Market-state accuracy | 6.0% | 25.6% | 25.0% | 100.0% |
| Coordination-protocol accuracy | 0.0% | 0.0% | 25.0% | 100.0% |
| Risk-control accuracy | 6.0% | 25.6% | 25.0% | 100.0% |
| Role-quorum accuracy | 0.0% | 0.0% | 25.0% | 100.0% |
| Value capture rate | 1.4% | 6.1% | 26.8% | 89.3% |
| Allocation score | 6.8 | 16.0 | 28 | 98 |
| Consensus score | 19.7 | 26.6 | 31.5 | 96 |
| Risk breach rate | 64.8% | 53.6% | 45.8% | 0.0% |

## Improvements

- Fully correct gain vs single agent: +100.0 pts
- Fully correct gain vs uncoordinated pool: +100.0 pts
- Fully correct gain vs static coordinated: +75.0 pts
- Coordination accuracy gain vs single agent: +100.0 pts
- Risk-control gain vs single agent: +94.0 pts
- Adversarial accuracy gain vs single agent: +100.0 pts
- Value capture gain vs single agent: +87.9 pts
- Decision-cycle reduction vs single agent: 99.3%
- Benchmark-implied value captured over single-agent baseline: $57,001,245,183.79
- Agent messages coordinated on holdout: 384,000

## RSI release history

- Gen 0: `baseline` — fully correct 0.0%, adversarial 0.0%, coordination 0.0%, risk breach 65.3% — released
- Gen 1: `skillos-adversarial-multi-agent-command-rsi-v1` — fully correct 8.3%, adversarial 13.4%, coordination 8.3%, risk breach 57.0% — released
- Gen 2: `skillos-adversarial-multi-agent-command-rsi-v2` — fully correct 16.3%, adversarial 26.3%, coordination 16.3%, risk breach 49.0% — released
- Gen 3: `skillos-adversarial-multi-agent-command-rsi-v3` — fully correct 24.7%, adversarial 39.8%, coordination 24.7%, risk breach 40.7% — released
- Gen 4: `skillos-adversarial-multi-agent-command-rsi-v4` — fully correct 32.7%, adversarial 52.7%, coordination 32.7%, risk breach 33.0% — released
- Gen 5: `skillos-adversarial-multi-agent-command-rsi-v5` — fully correct 41.3%, adversarial 59.7%, coordination 41.3%, risk breach 25.3% — released
- Gen 6: `skillos-adversarial-multi-agent-command-rsi-v6` — fully correct 49.7%, adversarial 59.7%, coordination 49.7%, risk breach 17.0% — released
- Gen 7: `skillos-adversarial-multi-agent-command-rsi-v7` — fully correct 58.3%, adversarial 59.7%, coordination 58.3%, risk breach 8.3% — released
- Gen 8: `skillos-adversarial-multi-agent-command-rsi-v8` — fully correct 66.7%, adversarial 59.7%, coordination 66.7%, risk breach 0.0% — released
- Gen 9: `skillos-adversarial-multi-agent-command-rsi-v9` — fully correct 74.7%, adversarial 72.6%, coordination 74.7%, risk breach 0.0% — released
- Gen 10: `skillos-adversarial-multi-agent-command-rsi-v10` — fully correct 83.0%, adversarial 86.0%, coordination 83.0%, risk breach 0.0% — released
- Gen 11: `skillos-adversarial-multi-agent-command-rsi-v11` — fully correct 91.7%, adversarial 100.0%, coordination 91.7%, risk breach 0.0% — released
- Gen 12: `skillos-adversarial-multi-agent-command-rsi-v12` — fully correct 100.0%, adversarial 100.0%, coordination 100.0%, risk breach 0.0% — released

## Final learned adversarial multi-agent coordination protocols

- **protocol_capacity_aware_enterprise_capture** — Accept enterprise demand only when specialist capacity, SLA quality, and unit economics clear the quorum gate.
- **protocol_api_ecosystem_with_abuse_guardrail** — Scale API distribution through metered access, partner controls, and platform-integrity safeguards.
- **protocol_retention_value_repair** — Stop growth spend until customer outcome value and retention risk are repaired.
- **protocol_data_moat_reinvestment** — Turn high-signal traces into compounding private skill releases without violating privacy boundaries.
- **protocol_cannibalization_guardrail** — Block growth campaigns that lift gross bookings but fail net-new incrementality.
- **protocol_high_revenue_margin_trap** — Avoid high-revenue work that destroys margin, capacity, or quality.
- **protocol_capital_allocation_frontier** — Choose the highest risk-adjusted compounding option rather than the superficially largest opportunity.
- **protocol_value_pricing_with_retention_guardrail** — Capture pricing power only where verified value and retention guardrails support it.
- **protocol_compute_shock_defense** — Defend margin under compute shocks through routing, caching, hedging, and validation.
- **protocol_service_to_product_compounding** — Convert repeated low-margin service work into validated reusable skill packages.
- **protocol_trust_gap_to_sales_evidence** — Turn enterprise trust gaps into reproducible validation evidence and safe sales collateral.
- **protocol_regulated_beachhead_sequence** — Enter regulated markets through bounded use cases, evidence packs, and claim discipline.
- **protocol_network_seed_anchor_demand** — Bootstrap network lanes by pairing anchor demand with reference skill supply.
- **protocol_attach_after_success** — Attach adjacent products only after core success milestones prove durability.
- **protocol_benchmark_to_sales_asset** — Turn proof gaps into repeatable trust assets that accelerate enterprise sales.
- **protocol_customer_registry_lock_in** — Create private registries where customer-specific traces compound into durable account value.
- **protocol_buyer_liquidity_before_supply** — Prevent supply overhang by activating buyer liquidity before funding more supply.
- **protocol_pipeline_quality_throttle** — Throttle low-quality pipeline before it consumes scarce specialist capacity.
- **protocol_channel_conflict_clean_room** — Resolve channel conflict through rules of engagement and margin-protected deal registration.
- **protocol_seasonal_spike_reservation** — Use capacity reservation and elastic pricing during seasonal spikes without harming retention.
- **protocol_validator_release_bottleneck** — Increase release throughput by automating repeatable validation while preserving quality.
- **protocol_preserve_clean_compounding_lane** — Recognize clean compounding lanes and avoid breaking them with unnecessary intervention.
- **protocol_partner_arbitrage_quality_gate** — Scale partner distribution only where acquisition advantage and quality guardrails are verified.
- **protocol_procurement_acceleration_pack** — Compress procurement by packaging security, ROI, and reference evidence with claim boundaries.

## Pre-registered pass/fail gates

- ✅ business domain adversarial multi agent coordination workflow
- ✅ large agent collective at least 160 agents
- ✅ specialist roles at least 20
- ✅ adversarial market trap classes at least 12
- ✅ compares single agent uncoordinated static and coordinated rsi
- ✅ proof receipts include commit sha 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
- ✅ no human review required
- ✅ no customers contacted
- ✅ no private data used
- ✅ no api keys required
- ✅ deterministic reproducible benchmark
- ✅ recursive self improvement releases at least 12
- ✅ rsi validation improves monotonically
- ✅ train cases at least 600
- ✅ validation cases at least 300
- ✅ holdout cases at least 1200
- ✅ final protocols at least 24
- ✅ fully correct gain vs single agent at least 85 points
- ✅ fully correct gain vs uncoordinated at least 70 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
- ✅ adversarial fully correct at least 99 percent
- ✅ value capture rate at least 85 percent
- ✅ allocation score at least 95
- ✅ consensus score at least 95
- ✅ risk breach rate zero
- ✅ material miss rate zero
- ✅ false intervention rate zero
- ✅ decision cycle reduction at least 95 percent
- ✅ benchmark implied value captured over single agent positive
- ✅ agent messages on holdout at least 250000

## Boundary

This is a 100% autonomous market-readiness benchmark using deterministic synthetic/redacted-style business data and benchmark assumptions. It is not audited customer ROI, live customer adoption, financial advice, investment advice, or a guarantee of future outcomes.

It proves that SkillOS can autonomously coordinate and recursively improve a large specialist-agent system on deterministic adversarial business benchmark cases.
