RAGF: Boundary Enforcement as Governance Infrastructure for Agentic AI in Regulated Systems
Year: 2026
Type: article
Abstract: Agentic AI systems deployed in regulated financial environments face a structural contradiction: their reasoning layer is probabilistic by construction, while the regulatory frameworks they must comply with — PSD2, 5AMLD, EU AI Act, DORA, MiCA, GDPR — demand deterministic, auditable, and reproducible enforcement. The model layer is now abundant; the execution control layer is not. Probabilistic guardrails, observability dashboards, and post-hoc audit logs cannot bridge this gap, because none of them intervene before consequence binds. This record archives a Fintech-domain instantiation of RAGF (Rule-Augmented Governance Framework), a deterministic runtime interception layer that enforces a strict architectural separation between the Thinking Engine (LLM or agent reasoner) and the Action Engine (a deterministic validator issuing ALLOW / ESCALATE / DENY verdicts under a fail-closed latency bound). Hallucinated verbs cannot reach regulated execution paths by construction; every candidate action is matched against a formal regulatory ontology before commitment. The Fintech appendix instantiates this architecture over European financial regulation, mapping agent actions to formal regulatory primitives across six EU regimes. It rests on the RAGF core, archived here as a source snapshot, whose contributions include: -A regulatory domain ontology (34 nodes, 52 relations, 19 invariants) mapping agent actions to regulatory primitives across PSD2, 5AMLD, the EU AI Act, DORA, MiCA, and GDPR.-An Agentic Maturity Model (AMM, levels 1–5) that conditions runtime enforcement on the demonstrated maturity of each agent class, replacing binary trust assumptions with a graduated authorisation surface.-An HMAC-chained audit trail compatible with TimescaleDB, providing tamper-evidence under an honest-key assumption and structurally addressing Article 12 (record-keeping) and Article 14 (human oversight) of the EU AI Act, alongside DORA ICT third-party registration, incident classification, and signed AI-BOM requirements.-An Adversarial Evolution Engine (AEE) used to evaluate the framework against 54 adversarial scenarios under a fail-closed policy. This Zenodo record archives the Fintech implementation appendix, the RAGF core specification it builds upon, and a source-code snapshot at the time of publication.
Author: Rodriguez Montaña, Yamil
Institution: RefleXion Medical (United States)
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161683047
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