An Agentic LLM-Based Framework for Population-Scale Mental Health Screening

2026-05-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Medical Devices & Health Technology · Depth: Advanced, quick

Summary

A novel agentic framework is proposed for building robust Large Language Model (LLM)-based pipelines specifically designed for population-scale mental health screening. This framework encapsulates each processing stage as a LangChain agent, governed by explicit policies and proxy-guided evaluation. Stages are incrementally locked upon validation, preventing overwrites without demonstrated improvement. The system evolves through feature-level exploration, proxy-based tuning, and freeze/rollback mechanisms, culminating in orchestration by an Orchestrator Agent that manages preprocessing, retrieval, selection, diversity, threshold optimization, and decoding. A proof-of-concept for transcript-based depression detection shows the framework converges to stable configurations, including cosine similarity, dynamic Top-k, and a 0.75 threshold, while effectively controlling evaluation costs and preventing regressions.

Key takeaway

For AI Architects and NLP Engineers developing healthcare AI solutions, this agentic framework offers a robust approach to building trustworthy and reproducible LLM pipelines. You should consider adopting incremental locking and proxy-guided evaluation to ensure stability and prevent regressions in sensitive applications like mental health screening. This method can significantly improve the adaptability and reliability of your AI systems when processing large clinical datasets.

Key insights

An agentic LLM framework enhances mental health screening by ensuring robust, adaptable, and trustworthy AI pipelines.

Principles

• Incremental locking prevents configuration regressions.
• Proxy-guided evaluation ensures stage validation.
• Orchestration centralizes complex pipeline management.

Method

The framework uses LangChain agents for each stage, governed by policies and proxy evaluation. Stages are incrementally locked, and an Orchestrator Agent coordinates preprocessing, retrieval, selection, diversity, threshold optimization, and decoding.

In practice

• Implement LangChain agents for pipeline stages.
• Use proxy-guided evaluation for validation.
• Apply freeze/rollback for configuration stability.

Topics

• Agentic LLM Framework
• Mental Health Screening
• LangChain Agents
• Clinical Data Processing
• Depression Detection

Best for: AI Architect, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.