EcoMind Agent

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  EcoMind Agent architecture uses AWS Bedrock, SageMaker and DynamoDB to analyze IoT data, automate energy optimization .

EcoMind Agent is an autonomous AI sustainability assistant designed to help organizations monitor, predict, and optimize their energy consumption. The inspiration came from the growing need for companies to reduce operational carbon footprints without hiring costly consultants or manually managing energy data.

Our team set out to build an intelligent, self-learning agent that can reason about energy usage patterns, connect to IoT and environmental data APIs, and automatically recommend or execute optimization tasks.

We built the system using Amazon Bedrock for reasoning with foundation models, Amazon SageMaker for training and deploying custom prediction models, and Amazon Bedrock AgentCore for orchestration and task automation. The agent integrates with external APIs and sensor data streams to provide actionable insights in real time.

During development, we learned how to coordinate multiple AWS AI services, manage prompt orchestration securely, and enable decision-making loops using reasoning LLMs. Our main challenges included integrating different data formats, optimizing inference latency, and designing autonomous behaviors that remain interpretable to users.

In the end, we created a scalable and intelligent energy optimization agent that can help organizations become more sustainable while reducing costs and environmental impact.

Built With

• agentcore
• amazon
• amazon-bedrock-(llms)
• amazon-dynamodb
• bedrock
• cloudwatch
• fastapi
• javascript
• lambda
• python
• react
• sagemaker