Sensor Insight Agent

General: Sensor Insight Agent

Ultimately agents can be configured and orchestrated to monitor, analyze, report, and/or assist on sensor/edge-type data sources. The necessary components:

• Sensor data: structured data from remote sensor locations
• Industry context/knowledge: the guidelines or principles that you want your agent to operate within (i.e. the water quality agent must act and report in accordance with EPA standards).
• Relevant tool suite: LLMs are not generally recommended for handling structured data, which presents a challenge with this use case. By equipping the agent with the relevant analytical tools and functions, we can open up the agents functionality to computational and analytical functions while maintaining confidence in the output and optimizing compute resources (i.e. calculators, pattern detection algorithms, forecasting models, etc.).

Problem

GDIT Hackathon use case example: Water Quality Agent

Two main challenges:

1. Monitoring and analysis of EPA contaminant thresholds across various Zones and water quality sensors
2. Detecting historical patterns or current trends indicative of future problems / maintenance needs

What it does

Water Quality Agent

During the hackathon, the team developed an agent that could provide a contamination report across various Zones in accordance with EPA standards and assist plant operators, field testers, and analysts.

• EPA could provide contamination analysis and alerts to water utilities with limited resources at no cost.
• Water utilities with limited budget for maintenance can use the data to quickly focus on potential issues and prioritize them.
• Water utility operators can be alerted about contamination issues quickly and inform the general public.
• Water utilities could use this foundational model for optimizing operations and maintenance and managing risk to public health.

General: Sensor Insight Agent

Sensor data spans many industries in commercial and government. The key to a successful agent will lie in the industry knowledge and expertise that can be provided alongside the proper set of tools for the agent to complete its tasks. Areas where a Sensor Insight Agent can be applied:

• IoT: anomaly detection, predictive maintenance for IoT networks like smart cities, smart homes, smart fleets (vehicles, vessels, etc.)
• Defense & Intel: SIGINT like radar and electronic warfare (EW) signals
• Cyber: incident response process, health monitoring

How it works (Powered by SeekrFlow™)

Water Quality Agent

This solution uses SeekrFlow’s modular AI architecture:

Planner: Parses user query, determines tool use (FileSearch or WebSearch), reports findings

FileSearch: Vector search pull relevant water quality sensor data to the user’s query

WebSearch: Validates sensor data with EPA standards

General: Sensor Insight Agent

As mentioned above, a proper Sensor Insight Agent would need to be equipped with other toolsets that cater to traditional computations and analysis to ensure confidence in the handling of the structured data. Therefore, an extensible solution would have the tools above in addition to tools like calculators, pattern and anomaly detection algorithms, etc.

Ideal Users

Water Quality Agent

• EPA Analysts
• Field testers
• Plant operators

General: Sensor Insight Agent

In most use cases, across industry, this agent would most likely be assistive to analyst and sensor operators. The solution can be scaled to include Command and Control (C2) functionality, meaning an additional C2 agent can be trained to prioritize, queue, and task sensors in a network based on insights coming from a mesh of Sensor Insight Agents.

Built on SeekrFlow

AI-Ready Data Engine: Sensor data chunked, embedded, labeled

FileSearch: Sensor data search relevant to user query

WebSearch: Validate sensor data to EPA standards

Agent Framework: Planner, Evaluator, Policy Check orchestration

React UI: Analyst-friendly reporting, visual highlights, clear scores

Value

Applying agentic AI to sensor data provides immense value to sensor operators and analysts who are often overloaded by the data volume and tasks at hand. While all of this was not developed during the hackathon, the benefits of implementing agents to sensor data are universal across industry and use case.

Enhanced situational awareness: An AI agent can process vast amounts of sensor data in real-time, providing analysts with a more comprehensive understanding of the environment, threat landscape, or system performance.

By analyzing data from various sensors, the AI agent can:

• Identify patterns and anomalies that may indicate potential security threats or system failures.
• Correlate data from multiple sources to provide a more accurate and complete picture of the situation.
• Alert analysts to potential issues before they become critical, enabling proactive measures to mitigate risks.

Predictive insights and early warning: By analyzing sensor data, an AI agent can identify early warning signs of potential problems, such as:

• Predictive maintenance: Detecting anomalies in equipment performance that may indicate impending failure, allowing for scheduled maintenance and minimizing downtime.
• Cybersecurity: Identifying suspicious patterns or behaviors that may indicate a potential cyber threat, enabling swift response and mitigation.
• SIGINT: Detecting unusual communication patterns or signals that may indicate enemy activity or intentions.

Automated anomaly detection and alerting: An AI agent can be trained to detect anomalies in sensor data, reducing the workload for analysts and sensor operators.

By automating anomaly detection, the AI agent can:

• Identify unusual patterns or behaviors that may indicate a potential security threat or system issue.
• Alert analysts to potential problems in real-time, enabling swift response and mitigation.
• Reduce false positives and minimize the noise from irrelevant data, ensuring that analysts focus on high-priority issues.

Improved sensor optimization and resource allocation: An AI agent can analyze sensor data to optimize sensor placement, configuration, and resource allocation.

By doing so, the AI agent can:

• Identify the most effective sensor configurations and placement strategies to maximize coverage and detection capabilities.
• Optimize sensor resource allocation, such as power consumption, bandwidth, or processing capacity, to minimize waste and ensure efficient operation.
• Provide analysts with recommendations for sensor optimization, enabling them to make data-driven decisions and improve overall system performance.

Where you can extend it

This Prebuilt Solution extends via SeekrFlow UI, SDK, or new data:

• Add more analytical tools for the agent to use when doing computations on the structured data
• Solidify reporting requirements for operators and analysts (create two personas if needed)
• Finetune a model on EPA standards rather than rely on WebSearch

Solution traits

Agentic | Retrieval-Augmented | Evaluable | Extendable

Takeaway

By leveraging an AI agent trained to analyze sensor data, analysts and sensor operators can monitor and report insights, improve situational awareness, and make more informed decisions to support their missions.