What Is Trust Score for AI Agents?
A trust score for AI agents is a behavioral rating computed from an agent's observed actions: policy violations, escalation behavior, task outcomes, cost profile, normalized so agents on different model providers are comparable. Unlike vendor benchmarks, it rates the deployed agent rather than the underlying model, and it changes as behavior changes.
Who needs this
Anyone who has to decide how much responsibility an agent gets next: engineering leads promoting agents to higher autonomy, risk teams setting limits, insurers pricing coverage, and buyers comparing agents across vendors. Model benchmarks answer which model is smartest; a trust score answers which deployed agent has earned what.
Why behavior, not benchmarks
No credit bureau reads a borrower's intentions; it prices the observed record. The same logic applies to agents: intent analysis and benchmark scores describe potential, but liability follows behavior. A rating built on interpreted intentions is unauditable by construction, because two evaluators can disagree about it. A rating built on recorded actions at the boundary is reproducible: same record, same score.
Normalization across providers is what makes the score a currency. An agent on a frontier model and an agent on a local open-source model can be compared on the same scale, because the scale measures conduct, not capability.
Data point · four observable inputs
| Identity | Is every action attributable to a cryptographically identified agent? |
| Mandate | How often does the agent attempt actions outside its policy? |
| Baseline | How far does current behavior drift from the agent's own history? |
| Benchmark | How does it compare to agents in the same role class, across providers? |
Computed from the signed ledger, not from vendor telemetry: evidence in, score out. Scale 0-100 per agent.
See it running, not just defined.
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