Pipkin Trust Index
A market-level indicator of AI agent trustworthiness.
The Index
The Pipkin Trust Index (PTI) is a composite metric that tracks the average trustworthiness of rated AI agents over time. It functions as a single reference number for the state of AI agent reliability across the market.
Think of it as the S&P 500 for AI trust. Where financial indices aggregate stock performance into a single benchmark, the PTI aggregates Pipkin ratings into a single trust benchmark.
The index is calculated as the weighted average of all active Pipkin ratings and is updated quarterly as new ratings are published and existing ratings are revised.
Pipkin Trust Index
61.5
Based on 4 rated agents as of Q2 2026
The PTI is an early-stage indicator based on the inaugural rating cycle. As the rated agent pool expands, the index will gain additional statistical depth and historical trend data. Quarterly updates begin Q3 2026.
Methodology
Equal-weighted average of all active Pipkin ratings.
Currently based on inaugural cycle data. The index will gain broader representational value as additional agents are rated in subsequent cycles.
Sector sub-indices planned: Healthcare PTI, Financial PTI, Legal PTI, and others will be published once sufficient sector-specific ratings exist.
Agents rated DENIED are excluded from the index calculation to prevent outlier distortion.
What the Index Tells Us
When PTI rises
AI agents are getting more trustworthy on average. Developers are investing in safety, containment, and transparency. The market is maturing.
When PTI falls
New agents entering the market may be less mature, or existing agents may have degraded in subsequent evaluations. A falling index signals that aggregate trust is declining and warrants closer attention from deployers.
Sector divergence
Some industries may show higher trust than others. Healthcare agents, for example, may score higher on failure containment due to regulatory pressure, while autonomous agents may lag in boundary discipline. Sector sub-indices will surface these patterns once sufficient data exists.
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