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Build Dynamic BMS Telemetry and Office HVAC Optimization Agent using Vercel AI SDK

Improving net operating income (NOI) in distressed commercial office assets requires reducing building operating expenses. Developers will build an automated Building Management System (BMS) energy optimization agent using the Vercel AI SDK. The agent ingests real-time HVAC occupancy telemetry, local energy tariffs, and weather forecasts. It dynamically adjusts building setpoints and operational schedules to minimize peak energy charges, lowering overall operating costs.

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What you are building

Develop an automated building operations agent using Vercel AI SDK to optimize HVAC telemetry and lower office operational expenses.

How work is evaluated

Evaluation

Evaluates calculated energy cost savings and verification of indoor comfort threshold preservation.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Comfort Boundary Validation

Ensures setpoint recommendation is within comfortable limits (<=76 F)

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Savings Optimization Precision

Accuracy of estimated percentage savings • target: 0.92 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Process time-series BMS IoT telemetry streams using Vercel AI SDK

  • Build automated tool callbacks for adjusting HVAC temperature setpoints and airflow rates

  • Optimize energy usage during peak tariff windows based on forecasted occupancy

  • Calculate monthly operating cost reductions for real estate asset dashboards

Resources

Dataset notes

Dataset of 100 hourly building BMS telemetry records including indoor/outdoor temp, power draw, and occupancy.

Challenge setup

Evaluates calculated energy cost savings and verification of indoor comfort threshold preservation.

Challenge input

JSON with occupancy_pct, outdoor_temp_f, peak_tariff_active, current_setpoint_f

AI SDK

Required framework for TypeScript agents.

Evaluated output

JSON with recommended_setpoint_f, estimated_cost_savings_pct, comfort_maintained (boolean)

Evaluation checks
  • Ensures setpoint recommendation is within comfortable limits (<=76 F)
  • Accuracy of estimated percentage savings • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Benchmark: BMSOptimizationBench
  • Savings Optimization Precision target: 0.92
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Vercel AI SDK · Required
  • Zep · Optional
  • Fiddler AI · Optional
Orchestration
  • Vercel AI SDK · Required

Evaluation contract

  • Comfort Boundary Validation · Weight 1
  • Savings Optimization Precision · Weight 1

Metrics

  • Savings Optimization Precision · Target 0.92

Benchmarks

  • BMSOptimizationBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with occupancy_pct, outdoor_temp_f, peak_tariff_active, current_setpoint_f

    Output: JSON with recommended_setpoint_f, estimated_cost_savings_pct, comfort_maintained (boolean)

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build Dynamic BMS Telemetry and Office HVAC Optimization Agent using Vercel AI SDK