Zhongsoft Rongxin Proposes Controlled Agent Architecture for Financial Regulation: Achieving Zero-Tolerance Deterministic Output via Knowledge and Data Foundations with State Machines

1 reports archived0 independent sourcesupdated 10/10/2026, 14:52:35

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To meet the zero-tolerance accuracy requirements of financial regulatory reporting, Zhongsoft Rongxin proposes restructuring AI Agents using four mechanisms: knowledge foundation, data foundation, controlled state machine, and full-chain traceability. This approach suppresses large model generation risks, transforming divergent Agents into strictly controlled systems that prioritize correctness over response, ensuring high-confidence decision loops.

LATEST/To meet the zero-tolerance accuracy requirements of financial regulatory reporting, Zhongsoft Rongxin proposes restructuring AI Agents using four mechanisms: knowledge foundation, data foundation, controlled state machine, and full-chain traceability. This approach suppresses large model generation risks, transforming divergent Agents into strictly controlled systems that prioritize correctness over response, ensuring high-confidence decision loops.

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InfoQ 中文T2·Oct 10, 14:5268 pts
Zhongsoft Rongxin Proposes Controlled Agent Architecture for Financial Regulation: Achieving Zero-Tolerance Deterministic Output via Knowledge and Data Foundations with State Machines
- Financial regulation demands extreme data accuracy, making the free-generation capability of general LLMs a core risk requiring strong constraints.
- Proposes a dual-drive model of 'Knowledge Foundation + Data Foundation' to ensure semantic consistency and bind authoritative source version snapshots.
- Employs controlled state machines to disable free reasoning paths, combined with full-chain traceability, achieving deterministic output where 'no answer is better than a wrong answer'.
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