ASTITAVA: An AI-Driven WebGIS for Scalable FRA Monitoring and Decision Support

We introduce ASTITAVA, a novel governance agent architecture designed to address systemic challenges in implementing the Forest Rights Act (FRA), 2006.We re-frame the complex administrative task as an autonomous agent problem.The agent's pipeline integrates a multi-stage perception module (OCR/NER) for ingesting and understanding unstructured legacy documents, a cognitive module for spatio-temporal reasoning (PostGIS integration and a Claim Health Scoring Model), and an action module (a hybrid ML-DSS) for optimized policy recommendations.A WebGIS atlas serves as a human-agent interface for explainability and oversight.Piloted on 15,000 claims, the ASTITAVA agent demonstrates a 67% reduction in processing time and 89.2% accuracy in eligibility prediction.This work contributes a tested, scalable framework for a new class of applied AI agents in complex digital governance.