// GLOSSARY -- SECURITY & COMPLIANCE

What is Agent Risk Scoring?

1 min read Updated

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.

HOW POLICYLAYER USES THIS

PolicyLayer assigns and tracks risk scores, dynamically adjusting spending limits based on observed behavior — earning trust through compliance.

FREQUENTLY ASKED QUESTIONS

How is the score calculated?
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.

FURTHER READING

Enforce policies on every tool call

Intercept is the open-source MCP proxy that enforces YAML policies on AI agent tool calls. No code changes needed.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
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