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Subject: I-D Action: draft-sato-soos-pt-03.txt
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Date: Sat, 05 Sep 2026 06:15:36 -0700
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Internet-Draft draft-sato-soos-pt-03.txt is now available.

   Title:   Progressive Trust (PT) for Agentic AI Governance Systems
   Author:  Tom Sato
   Name:    draft-sato-soos-pt-03.txt
   Pages:   37
   Dates:   2026-09-05

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 (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.

The IETF datatracker status page for this Internet-Draft is:
https://datatracker.ietf.org/doc/draft-sato-soos-pt/

There is also an HTMLized version available at:
https://datatracker.ietf.org/doc/html/draft-sato-soos-pt-03

A diff from the previous version is available at:
https://author-tools.ietf.org/iddiff?url2=draft-sato-soos-pt-03

Internet-Drafts are also available by rsync at:
rsync.ietf.org::internet-drafts