An AI-agent support service aimed at addressing challenges associated with the application-based approach in local-government welfare services has been integrated with the WebRings comprehensive administrative information system and is scheduled to launch in April 2027, according to reports. A model combining counter-service guidance with support for screening applications and procedures was described as a way to reduce the workload of administrative staff.
INES Corporation, which operates WebRings, a comprehensive administrative information system for local governments, announced an AI-agent support service for the welfare sector scheduled to launch in April 2027. WebRings was introduced around 2000 and is now operated by more than 160 organisations nationwide. It integrates functions for welfare areas including public assistance, long-term care insurance, disability welfare, child allowance and children's allowance.
The new service consists of two pillars. The first is a function that incorporates a generative AI agent into WebRings and provides cross-system guidance on the procedures required based on a resident's circumstances at the service counter. The second is a screening support function that assists with free checks and preliminary screening of application documents for long-term care insurance, disability welfare and child allowance. It uses a human-in-the-loop (HITL) approach, with the final screening decision made by a person.
A model was described for supporting staff across systems and application requirements in the 3 areas of long-term care insurance, disability welfare and child allowance, where procedural complexity has become an issue. In welfare services, challenges were identified that cannot be fully captured by conventional data, including the difference between cash and in-kind benefits, the diversity of screening criteria and the prevention of omissions and deficiencies in documents. The integration of contextual understanding and guidance through AI agents is expected to address these issues.
The article indicated that analysis of consultation data accumulated through AI and screening trends could eventually reveal latent needs in local communities and contribute to policymaking. At the same time, the importance of HITL-based operations for privacy protection and preventing misidentification, as well as data integration and operational reform in the field to ensure that residents facing gaps between systems are not overlooked, was emphasised.