Wildfire Relief Agent Orchestra

Complete End-End Wildfire Detection, Research, Smart Context Gathering, AI Agent Orchestra to improve wildfire response. Utilizes AWS Agentcore, Bedrock, S3, Google Gemini, Tavily MCP, Fire Map viz

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🚀 Inspiration

Every year, wildfires destroy millions of acres, displace thousands of people, and cause untold ecological and economic damage. Current detection systems often react after the damage has begun. I wanted to change that — to create an intelligent, automated, end-to-end disaster relief agent network that not only detects wildfires in real time but also coordinates immediate response.

Inspired by how multi-agent AI and LLM orchestration systems (like AWS AgentCore and Google’s Gemini) are transforming industries, I asked myself:

“What if agents could monitor live data, analyze anomalies, and instantly generate action plans — all autonomously?”

🧠 What I Built

My project is an AI-powered wildfire relief orchestration system that uses agents to perform each stage of disaster management:

  1. Detection Agent – Continuously monitors wildfire data streams (weather, thermal, satellite) and flags anomalies.
  2. Context Agent – Gathers nearby fire stations, evacuation zones, wind patterns, and previous similar cases from AWS DynamoDB.
  3. Analysis Agent – Uses Gemini + AWS Bedrock to compare the situation with historical cases and predict risk escalation.
  4. Action Agent – Generates a context-aware plan of action and triggers alerts via AWS SNS to local authorities and relief teams.
  5. Visualization Dashboard – Built on Streamlit, showing real-time data, alerts, and recommended actions.

⚙️ How I Built It

I structured the system as modular AWS agents connected through AgentCore and managed via MCP strands (for composable workflows):

  • Data Ingestion: AWS Lambda pulls real-time data from wildfire APIs and weather sources into S3.
  • Processing & Storage: Data is structured into JSON-like objects for DynamoDB, enabling efficient lookups.
  • Agent Reasoning: Gemini and Bedrock models perform reasoning, similarity matching, and action generation.
  • Communication: AWS SNS automates outbound alerts; Step Functions orchestrate multi-agent workflows.
  • Visualization: Streamlit dashboard displays the full pipeline, showing which agents are active and their outputs.

💡 What I Learned

  • How multi-agent frameworks like MCP and AgentCore can modularize complex systems.
  • Integration of AWS Bedrock and Gemini to blend reasoning, retrieval, and execution.
  • Importance of designing data pipelines for real-time monitoring and fail-safe response.
  • How to build scalable prototypes quickly using AWS Step Functions + DynamoDB.

🧩 Challenges I Faced

  • Managing data synchronization between multiple AWS services in near real-time and most importantly finding a way to integrate gemini with AWS!
  • Ensuring agents communicated efficiently without overflowing/too much context, summarizing the context that is passed by efficiently.
  • Balancing hackathon time constraints with complex AWS configurations.
  • Structuring data efficiently without relying on a vector DB due to time constraints, using clever DynamoDB schema design instead with a heuristic "vector" like column which summarized the data well enough .

🌟 Impact

This project demonstrates how next-gen AI orchestration can solve high-stakes real-world problems. By combining AWS Bedrock, Gemini, AgentCore, and MCP, to build a scalable foundation for AI-driven disaster relief, turning fragmented responses and data collection into a smooth, fast workflow.

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