Enterprise information systems remain strongly organized around persistent entities, CRUD operations, and prede- fined workflow states. These mechanisms are effective for deterministic transactions, but provide limited architectural support for interactions whose desired outcome is expressed as an evolving goal over heterogeneous context. Large Language Model (LLM) agents provide adaptive planning and tool orchestration, yet their probabilistic execution introduces challenges concerning bounded execution, authorization, state divergence, memory continuity, and au- ditable control. This paper proposes the Autonomous Relationship Engine (ARE), a Design Science Research (DSR) artifact centered on a persistent, time-bounded Intent Node as a unit of enterprise agentic execution. An Intent Node is modeled as I(t) = , where Gt represents an operational goal specification, Ct hard constraints, Pt runtime policy, Mt cognitive memory, and St authoritative execution-domain state. ARE distinguishes incoming intent from an active commitment and defines commitment-scoped action admissibility over the active commitment, authoritative state, action scope, and deterministic policy decision. Goal satisfaction is evaluated through Φ(Gt , St ), while candidate actions are generated through probabilistic planning and subjected to a Policy Decision Point (PDP) expressed through Policy-as-Code (PaC) before controlled execution. A tri-modal memory model provides episodic, semantic, and procedural context while remaining non-authoritative for authorization. The paper formalizes the commitment lifecycle, goal-state evaluation, bounded execution, policy enforcement, memory authority boundaries, and a reproducible experimental protocol, including a falsifiable ablation of commitment-linked cognitive-context re- construction after interruption, state change, policy update, and session resumption. The contribution is positioned as an architectural synthesis and formalization rather than a claim of novel individual mechanisms or experimentally established performance; the proposed evaluation criteria remain hypotheses for subsequent validation.
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Edison Ramón Sira Carreño (2026) studied this question.
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