MONTREAL.AI / SKILLOS

# Adversarial RSI Multi-Agent Market Command Center

Recursive self-improvement on large-scale specialist-agent coordination for profitable market capture.

Current status
PASSED_AUTONOMOUS_RSI_ADVERSARIAL_MULTI_AGENT_MARKET_COMMAND_CENTER_PROOF

160 agents. 20 specialist roles. 15 adversarial market-trap classes. No human review. No customers. No private data. No API keys.

160 coordinated agents
+100.0 pts gain vs single agent
100.0%adversarial accuracy
$57,001,245,183.79 benchmark-implied value over baseline

## Specialist agent collective

market intelligence pricing strategy margin architecture capacity planning risk governance regulatory boundary enterprise sales product packaging customer success data moat strategy validation science partner operations ecosystem design retention strategy operating finance procurement strategy security trust capital allocation growth experimentation coordination chair

## Recursive self-improvement curve

## Ablation: single agent vs uncoordinated pool vs static coordination vs SkillOS RSI

Metric | Single agent | Uncoordinated pool | Static coordinated | Coordinated RSI
--- | --- | --- | --- | ---
Fully correct decisions | 0.0% | 0.0% | 25.0% | 100.0%
Adversarial fully correct | 0.0% | 0.0% | 26.7% | 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%

## Proof receipts

- generated at utc: 2026-06-11T15:30:02Z
- repository: MontrealAI/skillos
- commit sha: ee50afb0a2057f1622690bb7a915b30e0f0efe2c
- ref name: main
- workflow: Autonomous RSI Adversarial Multi-Agent Market Command Center Proof
- run id: 27358141248
- run number: 18
- run attempt: 1
- run url: https://github.com/MontrealAI/skillos/actions/runs/27358141248
- benchmark seed: 20260530

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

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