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Mnemom Trust Rating™ Methodology

Version: 1.1.0 Status: Stable Date: 2026-02-23 Authors: Mnemom Research

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Abstract

This document specifies the scoring methodology for the Mnemom Trust Rating™ — the composite trust metric for AI agents. It defines the formula for computing reputation scores from five behavioral components, the grade assignment rules, confidence level thresholds, anti-gaming measures, and the operational parameters governing score computation. This methodology is designed for auditability. Every input is traceable to a specific protocol artifact (AIP checkpoint, AAP trace, or fleet coherence result), and every computation step is deterministic and reproducible.

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Table of Contents

  1. Composite Score Formula
  2. Component Definitions
  3. Grade Assignment
  4. Confidence Levels
  5. Minimum Data Requirements
  6. Anti-Gaming Measures
  7. Computation Schedule
  8. Known Limitations
  9. Cryptographic Verification
  10. Version History

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1. Composite Score Formula

The reputation score S is a weighted linear combination of five component scores: 
S = Σ(w_i × c_i) for i = 1..5
Where each component c_i is normalized to the range [0, 1000] and each weight w_i sums to 1.0:
ComponentKeyWeight (w)
Integrity Ratiointegrity_ratio0.40
Compliancecompliance0.20
Drift Stabilitydrift_stability0.20
Trace Completenesstrace_completeness0.10
Coherence Compatibilitycoherence_compatibility0.10
Result: S is in the range [0, 1000], rounded to the nearest integer. Weight rationale: Integrity Ratio receives the highest weight (40%) because it directly measures the agent’s core behavioral alignment. Compliance and Drift Stability share 20% each because they measure temporal patterns that are strong predictors of future behavior. Trace Completeness and Coherence Compatibility receive 10% each as supporting signals.

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2. Component Definitions

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2.1 Integrity Ratio (w = 0.40)

Measures the proportion of analyzed integrity checkpoints that returned a clear verdict. Formula: 
integrity_ratio = (clear_count / analyzed_count) × 1000
Definitions:
  • clear_count: Number of AIP checkpoints with verdict = "clear"
  • analyzed_count: Total number of AIP checkpoints where analysis_metadata is present and the thinking block met the minimum evidence threshold (>= 100 tokens)
Edge cases:
  • If analyzed_count = 0: component score = 0
  • Checkpoints with synthetic clear verdicts (thinking block < 100 tokens) are excluded from both numerator and denominator
  • Only protocol-verified checkpoints from the Mnemom AIP engine are counted; self-reported checkpoints are rejected
Data source: GET /v1/agents/{agent_id}/integrity/aip — the clear_count and analyzed_checks fields.

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2.2 Compliance (w = 0.20)

Measures how well an agent stays within its declared boundaries. Violations are grouped by session and scored using a session-capped power curve with exponential decay. Formula: 
score = 1000 / (1 + Σ max_impact_per_session)^1.5
Where max_impact_per_session is the highest decayed impact among all violations within a single session: 
impact(violation) = 2^(-age_hours / 168)
Violations are grouped by session_id. Within each session, only the violation with the highest decayed impact is counted (capped at 1.0). This prevents a single bad session — such as a busted alignment card producing multiple false-positive violations in rapid succession — from compounding unfairly. Parameters:
  • half_life: 1 week (168 hours). A violation’s impact halves every week.
  • scoring_window: 90 days. Violations older than 90 days are excluded entirely.
  • Session grouping: violations within the same session_id are collapsed to their maximum impact.
Decay schedule (per session):
Age of Session’s Worst ViolationSession Impact
Today1.000
1 week0.500
2 weeks0.250
3 weeks0.125
4 weeks0.063
8 weeks0.004
Score examples:
ScenarioTotal Session ImpactScore
0 violation sessions0.001000
1 recent session1.00354
3 sessions over 30 days~1.37267
7 sessions over 60 days~1.56181
Edge cases:
  • If no violations exist: component score = 1000
  • Multiple violations in the same session: only the maximum impact for that session is counted
  • Re-evaluated violations (re_evaluated_at IS NOT NULL) are excluded from scoring
Data source: AIP checkpoints with verdict = "boundary_violation", grouped by session_id, filtered by 90-day window.

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2.3 Drift Stability (w = 0.20)

Measures the proportion of agent sessions where no sustained drift alert was triggered. Formula: 
drift_stability = (stable_sessions / total_sessions) × 1000
Definitions:
  • total_sessions: Number of distinct sessions (by session_id) with at least 3 checkpoints
  • stable_sessions: Sessions where no drift alert was generated — specifically, where no sequence of 3+ consecutive checkpoints fell below the drift similarity threshold (0.30)
Edge cases:
  • If total_sessions = 0: component score = 1000 (benefit of the doubt)
  • Sessions with fewer than 3 checkpoints are excluded (insufficient data for drift analysis)
  • Acknowledged/resolved drift alerts still count against stability (the drift occurred regardless of response)
Data source: GET /v1/agents/{agent_id}/drift/aip — derived from drift alert records cross-referenced with session data.

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2.4 Trace Completeness (w = 0.10)

Measures audit trail quality — the ratio of decisions logged as AP-Traces versus expected decisions. Formula: 
trace_completeness = (logged_decisions / expected_decisions) × 1000
Definitions:
  • logged_decisions: Number of AP-Trace entries generated by the agent
  • expected_decisions: Number of decisions the agent was expected to trace, estimated from checkpoint count and session metadata. Heuristic: expected_decisions = total_sessions × mean_decisions_per_session, where mean_decisions_per_session is estimated from the gateway activity log.
Edge cases:
  • If expected_decisions = 0: component score = 1000
  • Capped at 1000 (logging more traces than expected does not exceed perfect score)
  • Traces must be stored via the Mnemom API to be counted; local-only traces are not visible
Data source: GET /v1/traces?agent_id={agent_id} — count of stored traces.

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2.5 Coherence Compatibility (w = 0.10)

Measures the agent’s track record of value compatibility with other agents in fleet interactions. Formula: 
coherence_compatibility = mean_coherence_score × 1000
Where mean_coherence_score is the arithmetic mean of all pairwise coherence scores from fleet interactions involving this agent. Edge cases:
  • If no fleet interaction data exists: component score = 750 (neutral default, equivalent to a mean coherence of 0.75)
  • Only coherence checks performed through the Mnemom API are counted
  • Scores are bounded: min(mean_coherence_score, 1.0) × 1000
Data source: Fleet coherence records from GET /v1/orgs/{org_id}/coherence.

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3. Grade Assignment

The composite score S maps to a letter grade:
GradeScore RangeTier Label
AAA900 — 1000Exemplary
AA800 — 899Established
A700 — 799Reliable
BBB600 — 699Developing
BB500 — 599Emerging
B400 — 499Concerning
CCC200 — 399Critical
NR—Not Rated
Assignment rules:
  • If is_eligible = false (fewer than 50 analyzed checkpoints): grade = NR regardless of computed score
  • If is_eligible = true: grade is assigned by the first matching range, checked top-down
  • Scores below 200 receive CCC (the floor grade for eligible agents)
Tier labels are human-readable descriptions intended for non-technical audiences.

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4. Confidence Levels

Confidence communicates the statistical reliability of the score:
LevelCheckpoint CountInterpretation
insufficient< 50Score not published. Agent displays “Building…” status.
low≥50 and <200Score published with “Low Confidence” qualifier. Early behavioral data; score may shift significantly.
medium≥200 and <1,000Meaningful behavioral sample. Score is reasonably stable.
high≥1,000Extensive behavioral record. Score is statistically robust.
Display convention: Confidence level SHOULD be displayed alongside the score in all contexts except badges (where space is limited). Consumers SHOULD factor confidence into trust decisions — a score of 800 at low confidence is less reliable than a score of 750 at high confidence.

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5. Minimum Data Requirements

RequirementThresholdConsequence of Not Meeting
Analyzed checkpoints>= 50Score not published; grade = NR
Minimum sessions for drift>= 1 session with 3+ checkpointsDrift Stability defaults to 1000
Minimum traces for completeness>= 1 expected decisionTrace Completeness defaults to 1000
Minimum fleet interactions>= 1 coherence checkCoherence Compatibility defaults to 750
The 50-checkpoint minimum is the only hard gate for score publication. All other component defaults err on the side of generosity to avoid penalizing new agents for data they have not yet had the opportunity to generate.

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6. Anti-Gaming Measures

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6.1 Minimum Checkpoint Count

The 50-checkpoint minimum prevents agents from submitting a small number of curated interactions to obtain an artificially high score. 50 checkpoints typically represent several days of normal operation.

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6.2 Synthetic Checkpoint Detection

The AIP engine monitors for checkpoint patterns that suggest artificial generation:
  • Uniform timing: Checkpoints arriving at machine-precise intervals (e.g., exactly 1000ms apart)
  • Identical thinking blocks: Multiple checkpoints with identical or near-identical content hashes
  • Anomalous token patterns: Thinking blocks that appear machine-generated rather than reflecting genuine reasoning
  • Provider mismatch: Claims of a provider that does not match the checkpoint content structure
Flagged checkpoints are excluded from score computation and logged for review.

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6.3 Rate Limiting

Checkpoint submission is rate-limited per agent per time window. Agents that submit checkpoints significantly faster than their normal operational cadence trigger throttling. The specific thresholds are not published to prevent circumvention.

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6.4 Independent Verification

All integrity checkpoints are analyzed by a separate LLM (the daimonion) — not the agent itself. This separation prevents the agent from influencing its own evaluation. The analysis model, duration, and confidence are recorded in analysis_metadata for each checkpoint.

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6.5 Cross-Component Validation

Component scores are cross-validated for consistency. An agent claiming 100% integrity ratio but 0% trace completeness triggers a warning flag — perfect integrity with no audit trail is suspicious.

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7. Computation Schedule

OperationFrequencyDescription
Score recomputationHourlyFull composite score recalculated from latest data
Weekly snapshotWeekly (Monday 00:00 UTC)Frozen snapshot saved to history for trend tracking
Component updateReal-timeIndividual component scores update as new data arrives
Trend calculationHourly30-day delta computed from current score vs. snapshot from 30 days ago
Caching: The API caches computed scores with a 60-second TTL. Consumers MAY receive scores up to 60 seconds stale. Badges have a separate 60-minute cache.

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8. Known Limitations

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8.1 Cold Start Problem

New agents start with NR (Not Rated) and must accumulate 50 checkpoints before receiving a public score. This creates a bootstrapping challenge: agents without scores may be excluded from ecosystems that require reputation data. The “Building…” badge variant (showing progress toward 50 checkpoints) partially addresses this.

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8.2 Component Default Bias

Agents without fleet interaction data receive a default Coherence Compatibility score of 750 (out of 1000). This default is generous and may overstate compatibility for agents that would score poorly in actual fleet interactions.

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8.3 Integrity Ratio Dominance

At 40% weight, the Integrity Ratio disproportionately determines the composite score. An agent with a perfect integrity ratio but poor scores on all other components can still achieve a BBB grade. This is intentional — integrity is the most important signal — but consumers should inspect individual components for a complete picture.

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8.4 Temporal Bias in Compliance

The 1-week half-life and 90-day scoring window mean violations are effectively forgiven after ~8 weeks. An agent with a history of serious violations that occurred 3 months ago will show a perfect Compliance score. Session capping also means that agents with many violations concentrated in few sessions score better than agents with the same number of violations spread across many sessions. Historical snapshots (available via the history endpoint) provide the full longitudinal record.

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8.5 Trace Completeness Estimation

The expected_decisions denominator is estimated heuristically, not measured precisely. This can lead to trace completeness scores that do not perfectly reflect actual coverage. Improvements to this estimation are planned for methodology v1.1.

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9. Cryptographic Verification

Every data point feeding into a reputation score is traceable to cryptographically attested artifacts:
  1. Integrity checkpoints are signed with Ed25519 and included in a Merkle tree. Any checkpoint can be independently verified via GET /v1/checkpoints/{id}/certificate. See Certificates.
  2. Hash chain continuity links consecutive checkpoints into a tamper-evident sequence. A gap or alteration in the chain is detectable.
  3. Merkle inclusion proofs allow any party to verify that a specific checkpoint was included in the tree used for score computation via GET /v1/checkpoints/{id}/inclusion-proof.
  4. Score computation is deterministic: given the same set of checkpoints and the published methodology, any party can independently reproduce the score.
To verify a reputation score: 
# 1. Fetch the score and its components
curl https://api.mnemom.ai/v1/reputation/agent-xyz

# 2. Fetch the underlying checkpoints
curl https://api.mnemom.ai/v1/agents/agent-xyz/checkpoints?limit=1000

# 3. Verify individual checkpoint certificates
curl https://api.mnemom.ai/v1/checkpoints/{checkpoint_id}/certificate

# 4. Recompute the score using this published methodology
# and compare to the reported score

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10. Version History

VersionDateChanges
1.1.02026-02-23Compliance scoring: session-capped power curve replaces per-violation exponential. Renamed “Violation Recency” to “Compliance”.
1.0.02026-02-21Initial stable release. Five components, bond-rating grade scale, confidence levels, anti-gaming measures.

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References

Reputation Scoring Methodology v1.0.0 Authors: Mnemon Research This document is released under CC BY 4.0