Spaces:
Sleeping
Sleeping
title: Efficiency Agent | |
emoji: ⚡️ | |
colorFrom: blue | |
colorTo: gray | |
sdk: gradio | |
sdk_version: 5.33.0 | |
app_file: app.py | |
pinned: false | |
license: apache-2.0 | |
short_description: Efficiency Across Industries | |
tags: | |
- agent-demo-track | |
- agent | |
- mistral-ai | |
# Efficiency Agent ⚡️️ | |
[](https://huggingface.co/) | |
[](https://gradio.app/) | |
## Agent Overview | |
This project is part of the [Agents-MCP-Hackathon](https://huggingface.co/Agents-MCP-Hackathon). This is a demo of an AI agent designed to assist industries and service providers in understanding and interpreting their operational metrics. The agent has access to real-time telemetry data that measures quality, downtime, and operational performance. | |
[See video overview](https://drive.google.com/file/d/1qa7wDxZWQlmktBauNlP8QxYFYG6he_3D/view?usp=share_link) | |
> [!NOTE] | |
> | |
> You can interact with the chatbot to gain insights and assistance on production-related queries. | |
> The chatbot will respond based on the current production data. | |
> 1. **Play** – Start the production simulation and generate synthetic data. | |
> 2. **Ask Agent** – Interact with the chatbot to get insights into the production process, identify issues, and more. | |
> | |
> **Note:** You can click on `Pause` or `Reset` to control the production simulation. | |
### Design | |
* The agent is implemented using **Mistral AI** via the **mistral-medium-2505** LLM. | |
* Its capabilities have been enhanced with a **chain-of-thought** reasoning process, allowing it to think, act, observe, and respond effectively to user queries. | |
* The agent is presented through a **Gradio interface**, which is well-suited for both real-time visualization and LLM interaction. | |
### Purposes | |
I took inspiration from my experience in the manufacturing industry, where understanding operational metrics is crucial for efficiency. More specifically, gaining precise insights from over 30 real-time telemetry metrics is a game changer, allowing teams to focus on critical areas for improvement and optimization. | |
Also, since the know-how is embedded in the agent, the risk of knowledge loss is minimized ensuring that valuable insights are retained and can be shared across the organization. Of course, this type of agent can be quickly adapted to various industry and service use cases such as manufacturing, cloud services, logistics, healthcare, and more. | |
> [!IMPORTANT] | |
> | |
> The demo is available on the [HF Space](https://huggingface.co/spaces/mriusero/efficiency-agent). | |
> If you want to run the demo locally, please add a `MISTRAL_API_KEY` & `AGENT_ID` to your `.env` file, credentials can be obtained from the [Mistral AI - La Plateforme](https://console.mistral.ai/) website. |