Last updated on March 4, 2026 by Editorial Team
Author(s): Pankaj Kumar
Originally published on Towards AI.
How we turned 20 years of government welfare rules into an AI-native, self-healing entitlement engine – with working code
The project is built entirely from publicly available information – official documentation, auditor reports, news articles and industry publications. No proprietary or confidential information was used.
The article discusses the migration of meritocracy curum CER eligibility rules to AI-native architectures, emphasizing the challenges of traditional migration methods that often fail due to poor rules documentation and reliance on legacy systems. The author introduces a reference implementation that involves using an OWL ontology, a Model Context Protocol (MCP) server, and an agentic orchestration layer, all designed to create a self-healing entitlement engine. The new strategy aims to make government welfare systems more auditable, transparent and maintainable, thus enabling policy analysts to adapt to changes more effectively without the need for specialized developers.
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