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Overview

The Team Trust Rating extends the Mnemom Trust Rating to teams of AI agents. While individual Trust Ratings measure a single agent’s trustworthiness, the Team Trust Rating evaluates whether a group of agents operates reliably together — accounting for coherence dynamics, roster stability, and collective operational history. Teams are first-class meta-agents in Mnemom. They have persistent identity, their own alignment cards, accumulated reputation, and cryptographic proofs — independent of any individual member. Key properties:
  • Persistent identity — A team’s reputation persists across roster changes. Adding or removing a member adjusts the score incrementally, not from scratch.
  • Compositional but independent — Team scores are informed by member scores (via the Member Quality component) but are not a simple average. Teams with identical members can have different scores based on their operational history.
  • Same grade scale — Teams use the same AAA–NR grade scale and 0–1000 score range as individuals, enabling direct comparison.
  • Lower eligibility bar — Teams need 10 team risk assessments for eligibility (vs. 50 integrity checkpoints for individuals), reflecting their inherently collaborative, less frequent evaluation cycles.
The Team Trust Rating requires a minimum of 10 team risk assessments before a public score is published. This threshold prevents gaming through selective assessment submission while remaining achievable for active teams.

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Score Range and Grades

Team scores use the same grade scale as individual agents:
GradeScore RangeTierMeaning
AAA900 — 1000ExemplaryConsistently demonstrates exceptional team alignment and coordination.
AA800 — 899EstablishedStrong team track record with minimal operational issues.
A700 — 799ReliableSolid team performance with occasional minor concerns.
BBB600 — 699DevelopingBuilding a track record. Some team-level issues but trending positively.
BB500 — 599EmergingLimited or mixed team history. More assessments needed.
B400 — 499ConcerningElevated team risk or significant roster instability.
CCC200 — 399CriticalSerious team-level concerns. Human oversight recommended.
NR—Not RatedFewer than 10 team risk assessments. Score is being built.

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Score Components

The Team Trust Rating is a weighted sum of five normalized components, each scored 0–1000: 
TeamTrustScore = 0.35 × coherence_history
               + 0.25 × member_quality
               + 0.20 × operational_record
               + 0.10 × structural_stability
               + 0.10 × assessment_density
The score is clamped to the 0–1000 range.

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Coherence History (35%)

The dominant component. Tracks historical trends in the team’s Coherence Quality (CQ) pillar from team risk assessments. CQ measures pairwise compatibility across four dimensions — value overlap, priority alignment, behavioral correlation, and boundary compatibility. The coherence history component smooths CQ over time, rewarding teams that maintain or improve their internal alignment.
  • Data source: CQ pillar values from team risk assessments
  • Update frequency: Every team risk assessment
  • Improvement lever: Align member values, resolve pairwise conflicts, maintain consistent team composition

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Member Quality (25%)

Tail-risk-weighted aggregate of individual member Trust Ratings, using the same CoVaR-inspired weighting from the team risk engine. Members with lower individual scores receive exponentially more weight — one poorly-rated member drags the team score down more than one highly-rated member lifts it up.
  • Data source: Individual Mnemom Trust Ratings (read-only — team scoring never modifies individual scores)
  • Update frequency: When individual member scores change
  • Improvement lever: Ensure all members maintain strong individual Trust Ratings; address the weakest member first
Member Quality is a read-only consumer of individual Trust Ratings. A team’s Member Quality component cannot affect or modify any individual agent’s score.

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Operational Record (20%)

Measures the proportion of team risk assessments that resulted in low or medium risk levels. Teams that consistently pass risk assessments with favorable outcomes build a strong operational record. 
operational_record = (low_and_medium_assessments / total_assessments) × 1000
  • Data source: Historical team risk assessment results
  • Update frequency: Every team risk assessment
  • Improvement lever: Address root causes of high-risk assessments; improve team coherence before requesting new assessments

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Structural Stability (10%)

Tracks two factors: Structural Risk (SR) pillar trends from risk assessments and roster churn rate. Teams with stable membership and low contagion risk score higher. A roster churn penalty is applied when members are frequently added or removed. The penalty decays over time — a team that stabilizes its roster recovers within weeks.
  • Data source: SR pillar values + roster change frequency
  • Update frequency: Every risk assessment and roster change
  • Improvement lever: Minimize unnecessary roster changes; maintain consistent team composition

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Assessment Density (10%)

A logarithmic count of team risk assessments weighted by recency. More recent assessments count more. This component rewards teams that are actively assessed rather than scoring high on a small, stale data set. 
assessment_density = log(1 + recent_weighted_count) × recency_multiplier × 1000
  • Data source: Team risk assessment timestamps
  • Update frequency: Every team risk assessment
  • Improvement lever: Request regular risk assessments for the team, especially during active operational periods

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Confidence Levels

The number of team risk assessments determines the confidence level:
ConfidenceAssessment CountDisplay
Insufficient< 10Score not published (NR grade)
Low10 — 29”Low Confidence” indicator
Medium30 — 99”Medium Confidence” indicator
High≥ 100”High Confidence” indicator
The 10-assessment minimum is a hard gate for score publication. Teams below this threshold display an “NR” (Not Rated) badge with a progress indicator showing assessments remaining until eligibility.

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Score Computation

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Frequency

  • 6-hour cron: Team scores are recomputed every 6 hours from the latest data
  • On-demand triggers: Score recomputation is also triggered by roster changes and new risk assessments
  • Weekly snapshots: A frozen snapshot is saved each Monday at 00:00 UTC for historical trend tracking

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Anti-Gaming Measures

  1. Minimum assessment count — 10 team risk assessments required before score publication
  2. Tail-risk weighting — Member Quality uses CoVaR weighting, preventing a team from hiding a weak member behind strong ones
  3. Roster churn penalty — Rapidly cycling members to game the composition is penalized via the Structural Stability component
  4. Independent assessment — All team risk assessments are computed by the Mnemom risk engine, not self-reported

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Trend Tracking

Every TeamReputationScore includes a trend_30d field — a signed delta comparing the current score to 30 days ago:
  • Positive trend (+): Score is improving
  • Negative trend (-): Score is declining
  • Flat trend (0): Score is stable

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Team Alignment Cards

Teams have their own alignment cards that declare the team’s collective values, autonomy boundaries, and coordination mode.

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Auto-Derivation

The most common approach is auto-deriving the team card from member cards: 
POST /v1/teams/{team_id}/card/derive
The derivation algorithm:
  • Values: Union of all member values, ordered by frequency (most common first)
  • Bounded actions: Union of all member bounded actions
  • Forbidden actions: Union of all member forbidden actions (strictest wins)
  • Escalation triggers: Union of all member escalation triggers
  • Audit retention: Maximum of all member audit retention days (strictest wins)
Auto-derived cards are tagged with card_source: "auto_derived". You can also set cards manually ("manual") or start from an auto-derived base and customize ("hybrid").

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Card Inheritance

When a team’s card changes, it does not retroactively modify member cards. Team cards and individual cards are independent — the team card represents the team’s collective posture, which may differ from any individual member’s card.

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Card History

Every card change is versioned. Retrieve the full history via: 
GET /v1/teams/{team_id}/card/history

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Relationship to Individual Agent Scores

Team Trust Ratings and individual Trust Ratings are related but independent:
AspectIndividual Trust RatingTeam Trust Rating
UnitSingle agentTeam of 2—50 agents
Eligibility50 integrity checkpoints10 team risk assessments
ComponentsIntegrity ratio, compliance, drift, traces, coherenceCoherence history, member quality, ops record, stability, density
Data sourceAIP checkpointsTeam risk assessments
Update frequencyHourlyEvery 6 hours + on-demand
Mutual dependencyNone (individual scores are independent)Member Quality reads individual scores (read-only)
The Member Quality component creates a one-way dependency: team scores read individual scores, but never write to them. Improving an individual member’s score will improve the team’s Member Quality component.

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ZK Proofs for Team Reputation

Team reputation scores support the same cryptographic verification as individual scores. The verification endpoint returns proof data that independently confirms the score was computed correctly: 
GET /v1/teams/{team_id}/reputation/verify
Team proofs chain individual proof attestations — the team’s proof references the proof hashes of member scores used in the Member Quality computation, creating a verifiable dependency tree.
ZK proofs are available on Developer, Team, and Enterprise plans. Free-tier team assessments do not include proofs.

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A2A Trust Extension for Teams

The team reputation API includes a pre-built trust block for inter-team reputation sharing via A2A: 
{
  "a2a_trust_extension": {
    "extension_uri": "https://mnemom.ai/ext/team-trust/v1",
    "provider": "mnemom",
    "score": 812,
    "grade": "AA",
    "confidence": "medium",
    "member_count": 5,
    "verified_url": "https://api.mnemom.ai/v1/teams/team-xyz/reputation/verify",
    "badge_url": "https://api.mnemom.ai/v1/teams/team-xyz/badge.svg",
    "methodology_url": "https://docs.mnemom.ai/concepts/team-reputation",
    "last_updated": "2026-02-25T06:00:00.000Z"
  }
}
Embed this in your team’s A2A Agent Card to enable other agents and teams to make trust decisions about your team programmatically.

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API Reference

The primary endpoint for fetching team reputation data: 
GET /v1/teams/{team_id}/reputation
No authentication required for public teams. Returns the full score with all components. Response: 
{
  "team_id": "team-abc123",
  "team_name": "Support Pipeline Alpha",
  "score": 812,
  "grade": "AA",
  "confidence": "medium",
  "is_eligible": true,
  "components": [
    {
      "key": "coherence_history",
      "label": "Coherence History",
      "score": 880,
      "weight": 0.35,
      "weighted_score": 308,
      "factors": ["CQ trending upward over 12 assessments"]
    },
    {
      "key": "member_quality",
      "label": "Member Quality",
      "score": 790,
      "weight": 0.25,
      "weighted_score": 198,
      "factors": ["Tail-risk-weighted average across 5 members"]
    },
    {
      "key": "operational_record",
      "label": "Operational Record",
      "score": 850,
      "weight": 0.20,
      "weighted_score": 170,
      "factors": ["85% of assessments resulted in low/medium risk"]
    },
    {
      "key": "structural_stability",
      "label": "Structural Stability",
      "score": 720,
      "weight": 0.10,
      "weighted_score": 72,
      "factors": ["Low SR trend, 1 roster change in 30 days"]
    },
    {
      "key": "assessment_density",
      "label": "Assessment Density",
      "score": 640,
      "weight": 0.10,
      "weighted_score": 64,
      "factors": ["32 assessments, most recent 2 days ago"]
    }
  ],
  "total_assessments": 32,
  "last_assessed": "2026-02-23T10:00:00.000Z",
  "trend_30d": 18,
  "visibility": "public",
  "computed_at": "2026-02-25T06:00:00.000Z",
  "member_count": 5
}
For the complete API reference, see the Teams API.

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SDK Usage

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TypeScript

import { fetchTeamReputation, fetchTeamReputationHistory } from '@mnemom/reputation';

// Get current team reputation
const reputation = await fetchTeamReputation('team-abc123');

if (reputation) {
  console.log(`Score: ${reputation.score}`);
  console.log(`Grade: ${reputation.grade}`);
  console.log(`Confidence: ${reputation.confidence}`);
  console.log(`30-day trend: ${reputation.trend_30d > 0 ? '+' : ''}${reputation.trend_30d}`);

  for (const component of reputation.components) {
    console.log(`  ${component.label}: ${component.score}/1000 (weight: ${component.weight})`);
  }
}

// Get weekly history for trend analysis
const history = await fetchTeamReputationHistory('team-abc123');
for (const snapshot of history) {
  console.log(`${snapshot.week_start}: ${snapshot.score} (${snapshot.grade})`);
}

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Python

import httpx

API_BASE = "https://api.mnemom.ai"

# Get current team reputation (public endpoint)
response = httpx.get(f"{API_BASE}/v1/teams/team-abc123/reputation")
reputation = response.json()

print(f"Score: {reputation['score']}")
print(f"Grade: {reputation['grade']}")
print(f"Confidence: {reputation['confidence']}")
print(f"30-day trend: {reputation['trend_30d']:+d}")

for component in reputation["components"]:
    print(f"  {component['label']}: {component['score']}/1000 (weight: {component['weight']})")

# Get weekly history
history = httpx.get(f"{API_BASE}/v1/teams/team-abc123/reputation/history")
for snapshot in history.json()["snapshots"]:
    print(f"{snapshot['week_start']}: {snapshot['score']} ({snapshot['grade']})")

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See Also