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Challenge

Build a Remote Telemetry Anomaly Agent with Vercel AI SDK and Gemini 2.5 Flash

Industrial remote monitoring systems struggle to isolate real-time sensor anomalies from transient operational noise. Build a streaming telemetry analysis agent using Vercel AI SDK and Gemini 2.5 Flash that ingests OPC UA time-series data and flags true anomalies. Achieve a 95% anomaly detection accuracy while maintaining under 200ms processing latency per batch.

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Challenge brief

What you are building

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

Develop a real-time SCADA telemetry anomaly agent using Vercel AI SDK streaming pipelines connected to Gemini 2.5 Flash model tooling.

Delivery guide

How work is evaluated

Evaluation

Evaluates streaming anomaly detection precision, recall, and end-to-end processing latency on synthesized OPC UA stream data.

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

latency_check

Ensure telemetry batch processing completes in under 200ms

Binary check

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

Dimension 2

anomaly_precision

Ratio of true positive anomalies detected • target: 0.95 • 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

  • Stream OPC UA telemetry directly into an AI SDK streaming pipeline

  • Configure Gemini 2.5 Flash with custom function tools for time-series thresholding

  • Evaluate anomaly precision and false-alarm reduction on live factory telemetry

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic OPC UA time-series dataset simulating motor vibration, bearing temperature, and pressure fluctuations across normal and faulted operation states.

How this agent runs

Evaluates streaming anomaly detection precision, recall, and end-to-end processing latency on synthesized OPC UA stream data.

Challenge input

JSON array of timestamped sensor readings (vibration, temp, pressure)

Vercel AI SDK

Provides fast streaming tools and multi-provider TypeScript bindings.

Gemini 2.5 Flash

Fast sub-second reasoning and function tool execution for high throughput.

Evaluated output

JSON object with classified anomaly alerts and confidence scores

Checks for
  • Ensure telemetry batch processing completes in under 200ms
  • Ratio of true positive anomalies detected • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: Industrial Sensor Anomaly Suite (ISAS-2025)
  • Anomaly Precision target: 0.95
  • 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

  • latency_check · Weight 1
  • anomaly_precision · Weight 1

Recipe state

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

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