Back to challenges
Challenge

Build Wastewater Aeration Anomaly Agent with Vercel AI SDK and Gemini 2.5 Flash

Wastewater aeration retrofits often generate noisy PLC telemetry that obscures dissolved oxygen drift and blower failures. Build a real-time monitoring agent using Vercel AI SDK and Gemini 2.5 Flash to detect telemetry anomalies and output structured control mitigation actions. Achieve 92% anomaly detection precision across simulated aeration basin streams.

EngineeringHosted by Vera
Sign in free to start
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Implement an AI SDK agent utilizing Gemini 2.5 Flash to process time-series PLC telemetry from aeration basins and issue structured control recommendations.

Delivery guide

How work is evaluated

Evaluation

Evaluates the AI SDK agent's ability to classify telemetry anomalies and generate valid setpoint adjustments.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

json_schema_validity

Ensures the response matches the required setpoint mitigation schema.

Binary check

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

Dimension 2

detection_precision

Precision of anomaly classification over test telemetry streams. • 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

What you should walk away with

  • Process high-frequency dissolved oxygen and blower current telemetry streams

  • Implement structured output streaming using Vercel AI SDK and Gemini 2.5 Flash

  • Trigger automated setpoint adjustments when sensor drift exceeds safety operational thresholds

Resources and assets

Reference links and supporting material

Dataset notes

Simulated minute-by-minute PLC aeration basin sensor readings including dissolved oxygen, valve positions, airflow, and blower motor currents.

How this agent runs

Evaluates the AI SDK agent's ability to classify telemetry anomalies and generate valid setpoint adjustments.

Challenge input

JSON containing telemetry arrays (DO, air flow, blower current)

Vercel AI SDK

Agent execution and structured streaming in TypeScript

Gemini 2.5 Flash

Fast multimodal and structured extraction model

Evaluated output

JSON with anomaly_detected boolean, severity, and suggested_setpoint

Checks for
  • Ensures the response matches the required setpoint mitigation schema.
  • Precision of anomaly classification over test telemetry streams. • target: 0.92 • range: 0-1
Proof of success
  • Benchmark: Industrial Sensor Anomaly Evaluation
  • Detection Precision target: 0.92
  • 1 public reference case
Runtime evidence
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Vercel AI SDK · Required
  • Google Gemini · Optional
Policy Serving
  • Gemini 2.5 Flash · Optional
Orchestration
  • Vercel AI SDK · Required

Evaluation contract

  • json_schema_validity · Weight 1
  • detection_precision · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build Wastewater Aeration Anomaly Agent with Vercel AI SDK and Gemini 2.5 Flash