Risk Assessment

Context-aware, real-time risk scoring for individual agents and teams — with optional zero-knowledge proofs

Risk assessment answers the question every platform operator asks before letting an agent act: how dangerous is this, right now, in this context?

Unlike static reputation scores, risk assessments are dynamic. The same agent can be low-risk for a data access request and high-risk for a financial transaction — because different actions stress different capabilities. Risk assessments incorporate the agent’s reputation, recent violation history, the specific action being attempted, and the operator’s risk tolerance.

For teams, the engine goes further: it evaluates whether a group of individually acceptable agents might still be dangerous when combined — through correlated failure, value divergence, or contagion dynamics.

Individual Risk

Each individual risk assessment produces a score between 0 and 1, computed as:

R = 0.60 × R_context + 0.30 × R_recency + 0.10 × R_confidence

Context-Aware Component Risk (60%)

Five reputation components are weighted differently depending on the action type:

Component	What It Tracks
Integrity Ratio	How often behavior matches declared values
Compliance	Adherence to organizational rules
Drift Stability	Whether behavior is changing over time
Trace Completeness	Whether full reasoning traces are provided
Coherence Compatibility	How well the agent works with the fleet

Different actions weight these differently. A financial_transaction emphasizes compliance (0.30) and integrity (0.30). A task_delegation emphasizes coherence (0.35) — can this agent hand off work reliably? A multi_agent_coordination action weights coherence highest (0.40).

Six action types are supported: financial_transaction, data_access, task_delegation, tool_invocation, autonomous_operation, and multi_agent_coordination. Each has a distinct weight profile tuned to the risks specific to that action.

Recency Penalty (30%)

Recent violations count more than old ones. The engine uses exponential decay with a 30-day half-life:

R_recency = sum(severity_weight x e^(-lambda x days_ago))

A critical violation yesterday contributes nearly 1.0. The same violation 30 days ago contributes 0.5. After 90 days, it is negligible. Severity weights range from 0.1 (low) to 1.0 (critical).

Confidence Penalty (10%)

Agents with limited behavioral history receive an uncertainty premium: insufficient data adds 0.30, low adds 0.20, medium adds 0.10, and high confidence adds nothing.

Risk Levels and Recommendations

The composite score maps to four risk levels. Thresholds shift based on the caller’s risk tolerance:

Tolerance	Low	Medium	High	Critical
Conservative	< 0.15	< 0.35	< 0.55	>= 0.55
Moderate	< 0.25	< 0.50	< 0.75	>= 0.75
Aggressive	< 0.35	< 0.60	< 0.85	>= 0.85

Each level maps to a recommendation: approve (low/medium), review (high, requires human approval), or deny (critical, block the action).

Team Risk

Team risk assessment evaluates whether a group of agents is safe to operate together. A team of individually low-risk agents can still be dangerous.

Three-Pillar Model

TeamCoherence = 0.30 x (1 - AQ) + 0.45 x CQ + 0.25 x (1 - SR)
TeamRisk = 1 - TeamCoherence

Aggregate Quality (AQ) uses tail-risk weighting inspired by CoVaR: agents with higher individual risk get exponentially more weight. One bad agent drags the score down far more than one good agent lifts it up.

Coherence Quality (CQ) evaluates pairwise compatibility across four dimensions — value overlap, priority alignment, behavioral correlation, and boundary compatibility. CQ penalizes high variance: a uniformly moderate team beats a volatile one where some pairs are excellent and others are terrible.

Structural Risk (SR) models failure contagion. For each pair of agents, SR estimates how much damage one agent’s failure would cause to the other, based on their coherence gap and the “value at risk.” The team’s SR combines the worst single-agent vulnerability with the fleet average.

Shapley Attribution

After computing team coherence, the engine attributes each agent’s marginal contribution using leave-one-out (LOO) Shapley values:

MC_i = TeamCoherence(all) - TeamCoherence(all without agent i)

Positive values mean the agent improves the team. Negative values mean they drag it down. This tells operators exactly which agents to swap, add, or remove to optimize team composition.

Historical team risk assessments are a primary input to the Team Trust Rating — specifically the Coherence History and Operational Record components. Teams that consistently receive low-risk assessments build stronger reputation scores over time.

Circuit Breakers

Hard safety floors override the continuous score when conditions are extreme:

• Any agent with reputation below 200 forces the team to critical/deny
• Any pairwise boundary compatibility below 100 forces critical/deny

Additional Analytics

Analysis	What It Detects
Outlier Detection	Agents whose risk exceeds the fleet mean by > 1 standard deviation
Cluster Detection	Groups of agents with correlated risk (shared failure modes)
Value Divergence	Values declared by some agents but missing in others
Synergy Detection	Whether the team score is better or worse than the average of individual scores

Team Recommendations

Score Range	Recommendation	Meaning
< 0.20	approve_team	Team operates as a unit
< 0.40	approve_team	Team operates with monitoring
< 0.60	approve_individuals_only	Individual actions allowed, joint operations blocked
>= 0.60	deny	All team operations blocked

Zero-Knowledge Proofs

Every risk assessment can be cryptographically proven correct without revealing the underlying reputation data. The entire risk computation is implemented in both TypeScript (for fast online use) and Rust (for ZK proof generation inside SP1 zkVM).

All arithmetic in the proving guest uses Q16.16 fixed-point integers — no floating-point operations exist in the proof circuit. This ensures perfect determinism across substrates.

Proofs are generated asynchronously: the risk score is returned immediately, and the proof follows. Once available, any third party can verify the proof without seeing the input data.

ZK proofs are available on Developer, Team, and Enterprise plans. Free-tier assessments do not include proofs.

Further Reading

• Reputation Scores — the input data that feeds risk assessments
• Team Trust Rating — team-level reputation built from team risk assessments
• Fleet Coherence — the pairwise coherence data used for team risk
• Integrity Checkpoints — how violations are detected
• Risk Engine Guide — step-by-step usage with SDK examples
• Security & Trust Model — the broader cryptographic verification pipeline