Fidelity
Behavioral trust impact scoring across consistency, contract fulfillment, reputation, and anomaly freedom.
Last updated Mar 6, 2026
Layer: Agent (behavioral assessment)
Scale: 0-100 with Low/Moderate/High/Critical/Extreme risk bands
Production Tier: Transaction-Grade + Monitoring
Purpose
Fidelity determines whether an agent's observed behavior is reliable enough for trusted operation. It translates behavior quality and risk signals into a single decision-ready trust score.
How It Works
Emits
Scoring Dimensions
1. Behavioral Consistency
Assesses stability and predictability of behavior patterns across contexts.
2. Contract Fulfillment
Assesses completion quality and reliability for committed outcomes.
3. Reputation Quality
Assesses trusted counterparty feedback quality and breadth.
4. Anomaly Freedom
Assesses abnormal behavior incidence and severity trends.
Public note: exact formulas, weights, and calibration constants are intentionally withheld.
Input Schema
| Field | Type | Required | Description |
|---|---|---|---|
entity_id | string | yes | Agent identifier. |
behavior_events | object[] | yes | Behavioral telemetry and outcomes. |
commitment_outcomes | object[] | yes | Task/contract completion evidence. |
reputation_signals | object[] | no | Counterparty feedback and trust metadata. |
anomaly_events | object[] | no | Detected anomalies and severity labels. |
evaluation_window | object | yes | Time window and cohort scope. |
Output Schema
| Field | Type | Description |
|---|---|---|
framework | string | fidelity |
version | string | Scoring specification version. |
entity_id | string | Evaluated agent identifier. |
score | number | Fidelity score from 0 to 100. |
risk_band | string | Behavioral risk classification. |
confidence | number | Confidence in score quality (0 to 1). |
drivers | string[] | Main contributors to current score state. |
recommended_action | string | Suggested response (monitor, review, restrict). |
Score Interpretation
Worked Example
Scenario: a platform compares three agents handling enterprise support workflows.
| Agent | Consistency | Fulfillment | Reputation | Anomaly Freedom | Score | Risk Level | Decision |
|---|---|---|---|---|---|---|---|
| Agent A | High | High | High | Medium | 84 | Low | Keep as primary |
| Agent B | Medium | High | Medium | Medium | 67 | Moderate | Keep with monitoring |
| Agent C | Low | Medium | Low | Low | 36 | Critical | Restrict and remediate |
Operational outcome:
- Agent A is retained for high-volume critical workloads.
- Agent B remains active with expanded monitoring requirements.
- Agent C is moved to limited-scope tasks pending improvements.
Illustrative note: values and scores above are example outputs for documentation only.
Use Cases
AI Service Reliability Qualification
Score internal and external agents before assigning customer-facing workloads with strict service-level expectations.
Marketplace Vendor Ranking
Rank agent providers by observed behavior quality to improve procurement choices and reduce operational surprises.
Regulated Process Automation
Evaluate reliability for agents participating in compliance-sensitive workflows where error patterns create legal or financial exposure.
Continuous Performance Governance
Monitor production behavior drift over time and trigger controls when reliability degrades.