Assigning dynamic risk scores to AI agents based on their behavior, transaction patterns, spending history, and policy compliance — used to adjust spending limits and monitoring intensity.
WHY IT MATTERS
Not all agents carry equal risk. A well-established agent with months of compliant behavior is lower risk than a newly deployed one. Risk scoring quantifies this difference.
Scoring factors include: agent age and track record, policy violation history, spending pattern consistency, operator reputation, and the complexity/value of transactions attempted.
Scores drive dynamic policy: low-risk agents get wider limits, high-risk agents get tighter controls and more frequent monitoring.
Every tool call decision logged, every policy versioned — the audit trail this page describes, by default.
Multi-factor: compliance history (violations?), behavioral consistency (predictable patterns?), operator reputation, and transaction risk profile. Weighted and combined into a normalized score.
Can scores improve?
Yes — consistent compliant behavior increases the score over time. PolicyLayer provides a trust-building pathway where agents earn expanded authority through demonstrated reliability.
Is scoring transparent?
Yes — PolicyLayer shows agents their current score and the factors influencing it. Operators see detailed score breakdowns for all their agents.
Route your MCP traffic through PolicyLayer. Every tool call is checked against your policy before it runs: allow, deny, or require approval. Per-identity grants. Full audit log. Live in minutes.