Governance-Constrained Agentic AI: Blockchain-Enforced Human Oversight for Safety-Critical Wildfire Monitoring

2026-04-05

AI Summary

Existing AI-based wildfire monitoring systems are prone to false alarms and trust deficits due to the absence of adaptive multi-agent coordination, structured human oversight, and verifiable accountability mechanisms. This work proposes a novel multi-agent architecture integrating blockchain-based governance constraints, formulating the monitoring task as a constrained partially observable Markov decision process (POMDP) with human authorization invariants. The system employs hierarchical coordination to dynamically schedule drones and enforces authorization policies via smart contracts on a permissioned blockchain. Crucially, human authorization is embedded directly into the agents’ decision loop as a state-transition invariant, enabling verifiable accountability. Experimental results demonstrate that the proposed framework maintains high detection performance while significantly reducing false alarm rates and exhibits robustness against injection, replay, and tampering attacks, all with only modest computational overhead.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The AI-based sensing and autonomous monitoring have become the main components of wildfire early detection, but current systems do not provide adaptive inter-agent coordination, structurally defined human control, and cryptographically verifiable responsibility. Purely autonomous alert dissemination in the context of safety critical disasters poses threats of false alarming, governance failure and lack of trust in the system. This paper provides a blockchain-based governance-conscious agentic AI architecture of trusted wildfire early warning. The monitoring of wildfires is modeled as a constrained partially observable Markov decision process (POMDP) that accounts for the detection latency, false alarms reduction and resource consumption with clear governance constraints. Hierarchical multi-agent coordination means dynamic risk-adaptive reallocation of unmanned aerial vehicles (UAVs). With risk-adaptive policies, a permissioned blockchain layer sets mandatory human-authorization as a state-transition invariant as a smart contract. We build formal assurances such as integrity of alerts, human control, non-repudiation and limited detection latency assumptions of Byzantine fault. Security analysis shows that it is resistant to alert injections, replays, and tampering attacks. High-fidelity simulation environment experimental evaluation of governance enforcement demonstrates that it presents limited operational overhead and decreases false public alerts and maintains adaptive detection performance. This work is a step towards a principled design paradigm of reliable AI systems by incorporating accountability into the agentic control loop of disaster intelligence systems that demand safety in their application.

Problem

• wildfire monitoring
• human oversight
• governance
• false alarms
• accountability

Innovation

• Governance-Constrained AI
• Blockchain-Enforced Oversight
• Constrained POMDP
• Risk-Adaptive Multi-Agent Coordination
• Human-in-the-Loop Authorization