Publication
AI-Intent: A Conceptual Modeling Framework for Accountable Multi-Agent AI Systems
Wolfgang Maaß; Iris Reinhartz-Berger
In: ER 2026. International Conference on Conceptual Modeling (ER-2026), 45th International Conference on Conceptual Modeling, located at ER 2026, October 5-8, St. John's, NL, Canada, Springer, 10/2026.
Abstract
Abstract. Existing agent-oriented conceptual modeling frameworks assume
components with deterministic execution semantics. Applied to
language model agents, whose outputs arise from probabilistic text generation
governed by natural-language inputs, they lack three constructs
that accountability requires: a binary declaration of each agent’s legitimate
action space; runtime enforcement of negative obligations by a
component external to the agent; and treatment of inter-agent communication
as auditable evidence.
To address these gaps, we introduce AI-Intent, a conceptual modeling
framework organized into three pillars: Declaration encodes each
agent’s action boundary, decision rights, capabilities, and risk policies in
Mandates; Enforcement evaluates every Proposed Action against the
applicable Mandate through a Compliance Agent before delivery; Auditability
derives a structured, durable Accountability Trace from every
session. A reference implementation for private investment advisory under
MiFID II is evaluated across 190 evaluation sessions: boundary violation
containment averaged 97.9% with zero forced-pass occurrences,
while audit trace completeness was found to depend on the instructionfollowing
capability of the deployed model.