Data Trust Scoring: The Missing Metric in AI Agent Observability

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Data Trust Scoring: The Missing Metric in AI Agent Observability

Published by HONEYPOTZ INC — AI agent observability

AI agent observability tools track everything: latency, token cost, accuracy, error rates. But they all miss the most important metric: data trust.

Your agent can be fast, cheap, and accurate — and still produce wrong results because it queried stale, uncertified, or unauthorized data.

Trust Scoring: The Fourth Pillar of Observability

Existing observability pillars:

Performance — latency, throughput
Cost — tokens consumed, API spend
Quality — accuracy, relevance

Missing: Trust — data certification, freshness, authorization

TrustGraph by HONEYPOTZ INC adds this fourth pillar. Before any agent query executes, TrustGraph resolves:

Certification status (1-100)
Freshness (hours since last update)
Authorization (policy-compliant?)
Composite trust score (0-100)

If trust score < 80, the query is blocked or flagged. Every resolution is logged for audit and observability dashboards.

Integration Is One API Call

import requests

trust = requests.get(
    f"http://trustgraph:8000/resolve/{source_id}",
    headers={"Authorization": f"Bearer {token}"}
).json()

if trust["score"] < 80:
    log.warning(f"Low trust: {trust['source']} score={trust['score']}")