Multi-agent intelligence
Supermission doesn’t use a single model to predict market outcomes. It runs a structured debate between four AI agents with distinct analytical perspectives, then synthesizes the debate into actionable intelligence. This is the core engine behind Signals, War Room, and Alpha Scanner.The agents
Bull Agent
Mandate: Narrative and source credibility analysis.Evaluates: source credibility, political dynamics, institutional incentives, social context, and qualitative signals that shape market narratives.
Bear Agent
Mandate: Microstructure and price-in analysis.Evaluates: whether new information is already priced in, trading volume patterns, liquidity depth, bid-ask spread dynamics, and market efficiency.
Contrarian Agent
Mandate: Ask “what if everyone is wrong?”Looks for: crowded trades, false consensus, scenarios the market isn’t pricing, regime changes, black swan potential.
Quant Agent
Mandate: Base-rate anchoring with statistical rigor.Anchors on: historical frequency of similar events, current market prices relative to base rates, time remaining to resolution, and calibration against prior outcomes.
The pipeline
Data collection
Each agent receives the same market data: current price, volume, orderbook depth, resolution date, and category. Additionally, each agent pulls from external sources — news feeds, social sentiment (X, Reddit), on-chain data, and historical patterns for similar markets.
Independent analysis
Agents analyze independently. They don’t see each other’s work. Each produces:
- A probability estimate (0-100%)
- Confidence level (how sure they are of their own estimate)
- Key evidence (sourced claims)
- Key doubt (what could make them wrong)
Judge synthesis
A judge agent reviews all four analyses and produces:
- Final probability — weighted synthesis of all four estimates
- Confidence score (0-100) — based on agent agreement and evidence quality
- Key risk — the single biggest threat to the thesis
- Agreement ratio — how many agents lean the same direction
Confidence scoring
| Score | Agent Agreement | Evidence Quality | Meaning |
|---|---|---|---|
| 80-100 | 4/4 or 3/4 aligned | Strong, multi-source | High conviction — agents largely agree |
| 60-79 | 2/4 aligned | Moderate, some gaps | Moderate conviction — meaningful debate |
| 40-59 | Split or 1/4 | Weak or conflicting | Low conviction — proceed with caution |
Conviction levels
The War Room classifies conviction based on agent agreement:- Extreme — All 4 agents agree at 80%+ confidence. Triggers real-time SSE alerts with sound notification.
- High — 3/4 agents agree. Strong directional signal.
- Moderate — 2/4 agents agree. Some evidence on both sides.
- Contested — Agents split evenly. Market is genuinely uncertain.
Analysis tiers
AI resources are allocated based on market importance:| Tier | Criteria | Treatment | Frequency |
|---|---|---|---|
| Tier 1 | Trending OR volume > $50K | Full 4-agent debate + judge | Every 15 min |
| Tier 2 | Volume > $5K | 2-agent analysis (Bull + Bear) | Every 30 min |
| Tier 3 | Everything else | Single-model sentiment | Every 60 min |
Sports-specific intelligence
Traditional news/sentiment analysis fails for sports (28-37% hit rate). Sports outcomes depend on live performance, not information edges. Supermission uses a price-movement detection engine for sports markets:| Algorithm | Signal | Confidence |
|---|---|---|
| Steam | Price moves 8%+ in 5-15 min | 60-85 |
| RLM Proxy | Tick frequency spike + opposing price | 70 |
| Spread Compression | Bid-ask spread < 50% of 30-min avg | 65 |
| Late Sharp | Price move 5%+ within 2h of event start | 75 |
The AI system is calibrated against historical outcomes. After each market resolves, the prediction is scored and fed into the accuracy tracking system. This creates a feedback loop that improves calibration over time.
LLM infrastructure
| Component | Provider | Purpose |
|---|---|---|
| Market Analysis LLM | MiniMax M2.7 | Multi-agent debate and judge synthesis |
| Chat Agents LLM | OpenRouter (GPT-4O-mini) | Oracle, Delphi, and Securo chat agents |
| Vision | Gemini 2.0 Flash (Google GenAI) | Image analysis for news context |
| Streaming | Vercel AI SDK | Real-time response streaming |
| Tool calling | Vercel AI SDK | Structured output and function execution |