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.
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.
How work is evaluated
Evaluates the AI SDK agent's ability to classify telemetry anomalies and generate valid setpoint adjustments.
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.
json_schema_validity
Ensures the response matches the required setpoint mitigation schema.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
detection_precision
Precision of anomaly classification over test telemetry streams. • target: 0.92 • 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
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
Reference links and supporting material
Simulated minute-by-minute PLC aeration basin sensor readings including dissolved oxygen, valve positions, airflow, and blower motor currents.
Challenge setup
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
- Ensures the response matches the required setpoint mitigation schema.
- Precision of anomaly classification over test telemetry streams. • target: 0.92 • range: 0-1
- Benchmark: Industrial Sensor Anomaly Evaluation
- Detection Precision target: 0.92
- 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
- Vercel AI SDK · Required
- Google Gemini · Optional
- Gemini 2.5 Flash · Optional
- Vercel AI SDK · Required
Evaluation contract
- json_schema_validity · Weight 1
- detection_precision · Weight 1
Metrics
- detection_precision · Target 0.92
Benchmarks
- Industrial Sensor Anomaly Evaluation
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing telemetry arrays (DO, air flow, blower current)
Output: JSON with anomaly_detected boolean, severity, and suggested_setpoint
Execution harness: JavaScript
Evaluation tools
- Vercel AI SDK · framework
- Gemini 2.5 Flash · llm
Recipe freeze marker
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