Our wildfire detection prototype emerged from a focused effort to engineer a simple, transparent agent that reacts to geospatial risk in real time. The agent is intentionally minimalist: it does not learn, it does not predict far into the future, and it does not rely on historical trends. Instead, it responds to the present moment, using structured environmental inputs to make binary decisions through a rule engine. This design reflects a deliberate hypothesis—namely, that real-time geospatial reflex agents can offer meaningful alerts even before more complex forecasting systems engage.
Wildfire risk begins with perceptual awareness. The agent collects and processes environmental signals such as thermal intensity, humidity, wind speed, vegetation density, land use classification, and proximity to nearby assets. These inputs are derived from synthetic or mock data sources that mimic satellite feeds, weather APIs, and land cover datasets. The goal is not to recreate Earth observation in full fidelity but to represent spatial risk factors in a form that is immediately usable by rule logic. Each percept corresponds to a key risk driver independently, with no requirement for aggregation or transformation. This atomicity ensures that every signal remains interpretable and auditable throughout the agent’s lifecycle.
At the core of the agent is its decision engine. This engine houses a small collection of human-readable rules that each test for combinations of environmental thresholds. For example, a rule might check for elevated temperature and low humidity in a forested area and return a message indicating critical risk. If multiple rules are triggered simultaneously, the decision escalates to the highest risk level among them. The agent also assigns a confidence score based on the number of rules that were triggered. Importantly, each rule is associated with a name, allowing decisions to include an explicit trace of which rules contributed to the alert. This design enforces clarity without introducing stochastic elements.
The agent follows a clear and deterministic control cycle. For each geographic coordinate it evaluates, the agent performs three operations: it perceives current conditions, applies its rule-based decision process, and acts by logging or outputting a structured alert. This cycle reflects a synchronous pattern of operation with no memory or internal state. That makes the agent suitable for repeated deployments on new locations or across a grid of spatial tiles. The absence of external dependencies further ensures that the agent can operate in edge environments with constrained connectivity or processing capabilities.
One of the agent’s defining strengths lies in its transparency. The rules it applies are both readable and tunable, enabling domain experts to adjust them without needing to retrain a model or interpret complex parameters. Alert outputs include the precise set of rules that triggered the decision and can be tuned via environment variables for factors like temperature thresholds or proximity cutoffs. Logging is implemented with industry-standard patterns to support both development and operational deployments. From a control perspective, this architecture allows fire analysts, emergency managers, or geospatial engineers to retain full authority over the behavior of the system.
Looking ahead, this reflex agent serves as the launch point for more complex forms of wildfire intelligence. Future versions will include model-based reasoning that can detect temporal trends, utility-driven agents that weigh trade-offs across competing priorities, and learning agents that refine rules based on observed performance over time. Spatial reasoning modules will be introduced to handle tasks like co-location analysis, hotspot mapping, and buffer evaluation. These capabilities will extend the agent from a local decision engine into a distributed, anticipatory system capable of informing broader incident response strategies.
By embedding decision rules directly into a spatial agent framework, we have demonstrated that meaningful wildfire alerts can be generated without requiring large-scale predictive models. This prototype proves that simple agents can be designed to act fast, speak clearly, and integrate seamlessly into geospatial workflows. It does not solve every problem. But it establishes a working principle: that reflexive, rule-based intelligence has a rightful role in the early stages of wildfire management.
Here’s to the spatial ones: Simple Reflex Agent for Wildfire Detection
