@techreport{sato-soos-pt-02,
    number =    {draft-sato-soos-pt-02},
    type =      {Internet-Draft},
    institution =   {Internet Engineering Task Force},
    publisher = {Internet Engineering Task Force},
    note =      {Work in Progress},
    url =       {https://datatracker.ietf.org/doc/draft-sato-soos-pt/02/},
    author =    {Tom Sato},
    title =     {{Progressive Trust (PT) for Agentic AI Governance Systems}},
    pagetotal = 36,
    year =      2026,
    month =     jun,
    day =       10,
    abstract =  {When a new employee joins an organization, they begin with limited authority. As they demonstrate good judgment -- completing tasks reliably, asking for guidance at the right moments, recovering well when things go wrong -- they earn greater trust and, with it, greater authority. If their performance degrades, or if months pass without any demonstration, that trust diminishes. This is how human organizations manage authority over time. AI agents have no equivalent mechanism. Today, an AI agent's authority is declared once in a credential at issuance time and does not respond to its behavioral record. An agent that has completed 200 successful sessions with a proven track record holds the same credential as a newly deployed agent. The human principal who issued both credentials made a judgment at issuance time; nothing that happened since is reflected in the agent's authority. This document defines Progressive Trust (PT): a behavioral trust model for AI agents in which authority recommendations evolve in response to cryptographically verified evidence of actual performance. PT measures five behavioral properties: whether the agent's self-assessed confidence matches its actual outcomes; whether it asks for human oversight at the right moments; whether it achieves its goals; whether it avoids decisions it later has to reverse; and whether it adapts when its action is rejected. These measures are derived exclusively from the tamper-evident, GEC-signed Event Stream -- an agent cannot influence its PT Score except through actual governed behavior. PT does not grant authority automatically. It generates structured recommendations, backed by behavioral evidence, for human principal review and approval. Human principals decide whether to elevate or reduce an agent's authority. PT ensures that decision is informed rather than made in the absence of history. Progressive Trust is the longitudinal complement of the Agent Execution Protocol {[}I-D.sato-soos-aep{]}: AEP governs what an agent does within a session; PT measures what an agent has done across sessions and translates that history into structured authority recommendations. No equivalent specification exists in IETF, ISO, NIST, or any agentic AI governance standards body.},
}