Accountable Agents - Declarative Control Architecture for Operational AI Systems

Abstract

Accountable Agents: Declarative Control Architecture for Operational AI Systems introduces a proposed architectural class of generative AI systems designed for environments in which model outputs may trigger tool use, workflow execution, state changes, external communication or organizational commitments. The paper argues that the central challenge of agentic AI is not autonomy itself, but accountable transition: the architectural control of when generative reasoning may become consequential action. While current agentic AI systems often focus on planning, tool use, memory and task decomposition, Accountable Agents shift the focus toward declared authority, external validation, verification before commit, final-state logic and auditability. Building on Declarative AI Architecture, the paper extends the principle of externalizing system logic from knowledge representation to operational authority. Declarative AI Architecture externalizes knowledge; Accountable Agents externalize authority. The proposed architecture defines agents not as free autonomous entities, but as declaratively configured operational roles governed by role artifacts, control artifacts, action classes, verification gates, reliability assessments, validator governance, commit boundaries and action provenance structures. The paper introduces core concepts such as the Operational Boundary Problem, the hidden authority problem, commit boundaries, multi-dimensional action classification, external validation, validator governance, architectural invariants, non-decision states and Operational Authority Governance. It also provides illustrative YAML-style artifacts, governance matrices, implementation risks and domain examples for healthcare, finance, legal, HR, industrial operations, public administration and cybersecurity. The contribution of the paper is to reframe agent safety from behavioral alignment alone to operational accountability. Safe agents are not merely better prompted, more aligned or more autonomous agents. They are systems that can demonstrate when they may act, when they must not act and when accountability requires restraint.