An Agentic AI Approach for Planning Improvement of Government Agency in Thailand

Strategic alignment between national blueprints and execution of government agencies is vital for a nation's development. However, Thailand's traditional action plans remain a manual, "inward-looking" style that often results in activity-based key performance indicators (KPIs) focused on budget disbursement rather than tangible outcomes. This paper introduces an agentic AI approach to assist public policy planning by utilizing large language models (LLMs) as an autonomous "coworker". Implemented on a no-code agentic AI platform, the workflow automates data extraction from current national strategy reports, identifies gaps, and synthesizes structured action plans. A proof of concept demonstrates that this agentic approach can successfully assist in proposing "meaningful KPIs" and associated ministries for joint KPIs that shift from routine administrative tasks to proactive, outcome-oriented benchmarks aligned with global indices and national strategic goals. The results indicate that agentic AI has potential in reducing human effort and organizational silos and providing a scalable model for modernizing government administration.