Renewable Energy Offset Verification Workflow with Mastra AI and Grid Telemetry
As clean energy additions put China's national carbon emissions into reverse, corporate carbon accounting requires auditable workflows to verify whether industrial electricity off-takes correspond to genuine renewable generation versus thermal standby capacity. Manual auditing of generation logs and smart meter data creates compliance delays and greenwashing risk. In this challenge, you will implement an agentic auditing workflow using Mastra AI in TypeScript. Mastra AI's workflow engine will orchestrate multi-step retrieval, calculate hourly carbon intensity factors, verify additionality principles, and log immutable proof records. Your solution will execute stateful workflows with step recovery, query grid generation metrics, and emit certified renewable energy offset decisions.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an auditable carbon offset verification workflow using Mastra AI to process clean energy generation telemetry and grid emissions factors.
How work is evaluated
Evaluates Mastra AI workflow execution accuracy when verifying clean energy offset transactions.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
workflow_completion
Mastra AI workflow executes all steps to completion without error
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
offset_kg_accuracy
Accuracy of calculated net CO2 avoided in kilograms • target: 0.99 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement multi-step stateful workflows using Mastra AI.
Calculate hourly grid carbon intensity offsets from generation data.
Ensure robust error handling and step retries within workflow execution.
Generate immutable offset compliance audit logs.
Reference links and supporting material
Synthetic time-series dataset of hourly facility energy consumption and regional clean energy generation fractions.
Challenge setup
Evaluates Mastra AI workflow execution accuracy when verifying clean energy offset transactions.
Challenge input
JSON with facility_id, hourly_consumption_kwh, solar_offtake_kwh, regional_grid_co2_intensity
Mastra AI
Mandatory TypeScript workflow and agent execution platform.
Evaluated output
JSON with verified_offset_co2_kg, additionality_verified (bool), compliance_status
- Mastra AI workflow executes all steps to completion without error
- Accuracy of calculated net CO2 avoided in kilograms • target: 0.99 • range: 0-1
- Benchmark: CarbonAccountingBench
- Offset Kg Accuracy target: 0.99
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Mastra AI · Required
- Seldon Core · Optional
- Llama 3.2 · Optional
- Mastra AI · Required
Evaluation contract
- workflow_completion · Weight 1
- offset_kg_accuracy · Weight 1
Metrics
- offset_kg_accuracy · Target 0.99
Benchmarks
- CarbonAccountingBench
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with facility_id, hourly_consumption_kwh, solar_offtake_kwh, regional_grid_co2_intensity
Output: JSON with verified_offset_co2_kg, additionality_verified (bool), compliance_status
Execution harness: JavaScript
Evaluation tools
- Mastra AI · Workflow Engine
Recipe freeze marker
No recipe freeze time is recorded.