Reasoning with o1

Start

Because one agent isn’t enough to conquer complexity.

Agent Armada

Vikashkodati

Tim Huff

theorem-stack

Agent Armada is an AI multi-agent system designed to enhance search and rescue operations during natural disasters. By utilizing a Large Language Model (LLM) as a user-friendly interface, it enables dynamic interaction between human operators and a swarm of autonomous agents, allowing for rapid and efficient searches in challenging terrains while minimizing risks to personnel.

The LLM interprets user commands and orchestrates agent activities in real time, streamlining communication and enhancing situational awareness, which improves response times and operational effectiveness compared to traditional methods.

Beyond disaster response, Agent Armada has applications in military reconnaissance, environmental monitoring, and other critical scenarios. Its strength lies in operating without pre-programmed logic, as the LLM adapts its actions based on real-time information and human input. The framework allows the LLM to interact with 3D geospatial environments through a swarm of agents, enhancing decision-making in complex terrains.

This flexibility enables Agent Armada to tackle a wide range of challenges, from monitoring environmental changes in national parks to intelligence gathering in unfamiliar areas. By continuously learning from operator interactions and environmental feedback, it improves its performance and adaptability, providing timely support in diverse, high-stakes situations and enhancing operational efficiency and safety across various fields.

Agent Armada is an AI multi-agent system that enhances search and rescue operations during natural disasters by using a Large Language Model (LLM) for intuitive communication with autonomous agents.

"Agent Armada team on Reasoning with o1 Hackathon"

Team Idea

Submission