Sentinel-X

Sentinel-X is a multi-agent autonomous ecosystem for detecting, analyzing, and responding to environmental crises, with a focus on illegal deforestation. Instead of operating as a passive dashboard, the platform uses an agentic workflow in n8n to coordinate specialized AI roles that gather evidence, verify context, and generate structured policy responses aligned with climate and land-protection goals.

This repository contains the browser-based command interface, a lightweight Node.js proxy for local webhook communication, and the exported n8n workflow that powers the agent orchestration layer.

Abstract

Sentinel-X is a multi-agent autonomous ecosystem that detects, analyzes, and responds to environmental crises, specifically illegal deforestation, in real time. Moving beyond passive dashboards, Sentinel-X uses n8n's visual agentic workflows to coordinate two specialized AI agents: a Field Researcher Agent for data gathering, verification, and modeling, and a Policy Director Agent for decision-making, compliance framing, and alert routing. The system is designed for resilience, autonomy, and ethical governance, ensuring that automated environmental action remains accountable and aligned with international norms.

Core Idea

The project is designed as an operational console for environmental intelligence:

• The frontend lets an operator inject a crisis scenario, monitor agent execution, and inspect structured policy outputs.
• A local Node.js proxy forwards the frontend request to an n8n webhook while handling browser CORS.
• The n8n workflow orchestrates two AI agents in sequence.
• The final response is returned to the interface as structured JSON for clear operational review.

System Architecture

Front End

1. Field Researcher Agent

The Field Researcher Agent is responsible for collecting and validating situational context. In the exported n8n workflow, this agent is connected to:

• SerpAPI for search-based context gathering
• Wikipedia for background validation
• Calculator for resource and logistics calculations
• HTTP Request for external data retrieval

Its job is strictly evidence-focused: gather facts, identify anomalies, and prepare structured research for downstream policy reasoning.

2. Policy Director Agent

The Policy Director Agent consumes the verified research summary and produces a strict JSON response containing:

• threat_level
• sdg_alignment
• primary_risk
• immediate_action
• policy_brief

The workflow instructs this agent to align outputs with:

• SDG 13 Climate Action
• SDG 15 Life on Land

Tech Stack

• HTML for the interface structure
• CSS and custom styling for the visual system
• JavaScript for the UI logic and response rendering
• Tailwind CSS CDN for utility-first layout and styling
• Node.js for the local proxy server
• n8n for visual multi-agent orchestration
• Groq-hosted models in the exported workflow

Repository Structure

project_ananta_chkara/
|- index.html             # Main Sentinel-X interface
|- style.css              # Supplemental visual styles
|- server.js              # Local CORS/webhook proxy
|- n8n safe (1).json      # Exported n8n multi-agent workflow
|- assets/
|  |- sentinelx-overview.svg
|  `- sentinelx-workflow.svg
`- README.md

Frontend Highlights

The web interface is styled as a cyber-operations dashboard and includes:

• Scenario injection panel for environmental crisis prompts
• Terminal-style orchestration log for agent execution feedback
• Policy output panel for rendered JSON responses
• Crisis override switch for injecting dynamic constraints
• Status indicators for execution, transmission, and response state

How It Works

1. A user enters a crisis scenario in the web interface.
2. The browser sends the scenario to the local proxy at http://localhost:3000/chat.
3. The proxy forwards that payload to the n8n webhook.
4. The Field Researcher Agent gathers and validates information using connected tools.
5. The Policy Director Agent transforms the findings into a structured governance response.
6. n8n returns the JSON output to the frontend.
7. The interface renders both the raw response and a readable policy summary.

Getting Started

Prerequisites

• Node.js 18+
• n8n running locally
• Access to the APIs configured inside the n8n workflow, such as Groq and SerpAPI

Run the Project

1. Start n8n and import n8n safe (1).json.
2. Confirm the webhook path in server.js matches your imported workflow.
3. Start the proxy server:

node server.js

4. Open index.html in a browser, or serve the folder with any simple static server.
5. Enter a crisis scenario and execute the agents.

n8n Workflow Notes

The included workflow export demonstrates a clean sequential agent pattern:

• Webhook receives the incoming prompt
• Agent: Field Researcher performs evidence collection
• Agent: Policy Director creates a structured policy response
• Respond to Webhook returns JSON to the frontend

This makes the project a strong example of using n8n not just for automation, but for accountable agent orchestration.

Use Cases

• Illegal deforestation monitoring
• Environmental compliance response simulation
• Crisis governance prototyping
• AI policy-routing demonstrations
• Multi-agent orchestration showcases for hackathons, portfolios, and research demos

Why This Project Stands Out

Sentinel-X combines visual storytelling, live agent orchestration, and mission-driven AI design in one compact prototype. It demonstrates how frontend interfaces, lightweight backend glue, and no-code agent workflows can work together to build systems that are both technically compelling and socially relevant.

Future Improvements

• Integrate live geospatial satellite or GIS data
• Add authentication and role-based operator access
• Store incident history and response logs
• Add real alert routing through email, messaging, or emergency channels
• Expand the ethical governance layer with explicit approval workflows

License

This project is licensed under the MIT License. See the LICENSE file for details.

Author

Rohit Jain
LinkedIn: https://www.linkedin.com/in/546-rohit-jain