---
id: "hardman-syntelos-a-hierarchical-taxonomy-of-intent"
type: "paper"
type_label: "Papers and paper collections"
title: "Syntelos: A Hierarchical Taxonomy of Intent in Digital Interactions"
maintainer: "Daniel Hardman"
authors:
  - "Daniel Hardman"
date: "2025-12-11"
year: 2025
reading_level: "practitioner"
reading_level_label: "Practitioner"
reading_level_meaning: "architects, product people and adopters who know identity systems"
evidence: "informative"
evidence_meaning: "an author's paper, deck, tutorial or post explaining the suite"
status: "quiet"
status_meaning: "no change in six to eighteen months; still correct"
access: "public"
access_meaning: "anyone with the link"
topics:
  - "theory"
  - "governance"
roles:
  - "security-architect"
  - "standards-policy"
  - "researcher"
source_url: "https://dhh1128.github.io/papers/syntelos.html"
source_license: null
source_license_note: null
found_on: "hardman-papers"
kerisse_credit: false
abstract_license: "CC BY 4.0"
abstract_license_url: "https://creativecommons.org/licenses/by/4.0/"
abstract_attribution: "KSKB, the KERISuite Knowledge Base (https://kskb.dev/)"
last_checked: "2026-10-15"
kskb_url: "https://kskb.dev/cards/hardman-syntelos-a-hierarchical-taxonomy-of-intent/"
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---

# Syntelos: A Hierarchical Taxonomy of Intent in Digital Interactions

A 2025 paper by Daniel Hardman proposing Syntelos, a hierarchical, machine-readable taxonomy of the intent (telos) of interactions, rooted in shared outcomes and drawing on Activity Theory and commitment protocols, for constraining delegation and negotiating interactions between human and AI agents. It surveys existing standards (industry and commodity codes, agent communication languages, Schema.org, privacy vocabularies, IEEE 7012) and proposes top-level categories such as /relate, /share, /trade and /govern.

Labels: reading level Practitioner; evidence informative (an author's paper, deck, tutorial or post explaining the suite).
Source: https://dhh1128.github.io/papers/syntelos.html (license not recorded).
The abstract is KSKB's own text under Creative Commons Attribution 4.0 (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/; credit KSKB, the KERISuite Knowledge Base (https://kskb.dev/).