BESS Health Monitoring Agent
As Battery Energy Storage Systems (BESS) mature, moving from commissioning to operational longevity requires sophisticated predictive maintenance. This challenge tasks you with building a Mastra AI-powered agentic workflow that analyzes high-frequency telemetry data (voltage, temperature, state of charge) to detect early-stage cell degradation and thermal anomalies. You will integrate CodeCarbon to measure the environmental impact of your AI inference and training cycles, ensuring that the 'Green AI' solution does not consume excessive energy while monitoring renewable assets. This aligns with the industry's shift toward bridging factory quality controls with real-time site operations.
What you are building
The core problem, expected build, and operating context for this challenge.
As Battery Energy Storage Systems (BESS) mature, moving from commissioning to operational longevity requires sophisticated predictive maintenance. This challenge tasks you with building a Mastra AI-powered agentic workflow that analyzes high-frequency telemetry data (voltage, temperature, state of charge) to detect early-stage cell degradation and thermal anomalies. You will integrate CodeCarbon to measure the environmental impact of your AI inference and training cycles, ensuring that the 'Green AI' solution does not consume excessive energy while monitoring renewable assets. This aligns with the industry's shift toward bridging factory quality controls with real-time site operations.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Agentic Tool Validation
Ensures the Mastra AI agent successfully calls the 'DegradationModel' tool.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Prediction Mean Absolute Error
MAE for Remaining Useful Life (RUL) • target: 10 • range: 0-50
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Mastra AI's Workflow system to chain data ingestion, analysis, and notification tools.
Implement CodeCarbon decorators to monitor energy consumption during model inference on large BESS datasets.
Build a Battery Management System (BMS) anomaly detection tool using Scikit-Learn or PyTorch.
Design a RAG (Retrieval-Augmented Generation) system within Mastra AI to query technical battery manuals for troubleshooting steps.
Optimize agentic tool-calling to minimize latency and computational overhead in grid-edge environments.
Deploy a Mastra AI server that exposes endpoints for real-time telemetry ingestion and health reporting.
How this agent runs
The solution will be evaluated based on the accuracy of degradation predictions, the efficiency of the agentic workflow, and the integration of carbon tracking.
Challenge input
CSV file containing 50 cycles of voltage/current/temp data.
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
JSON containing predicted SoH (State of Health) and RUL in cycles.
- Ensures the Mastra AI agent successfully calls the 'DegradationModel' tool.
- MAE for Remaining Useful Life (RUL) • target: 10 • range: 0-50
- Prediction Mean Absolute Error target: 10
- 2 public reference cases
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Agentic Tool Validation · Weight 1
- Prediction Mean Absolute Error · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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