Pipeline Telemetry Anomaly Detection Agent using Google ADK and Hugging Face
Crude oil pumped to export hubs must maintain strict pressure and flow metrics to prevent leaks or theft during the 3-month export window. Build a real-time pipeline monitoring agent using Google Agent Development Kit (ADK) with custom anomaly detection models hosted on Hugging Face Inference API. The agent processes stream telemetry (SCADA flow rates, pressure differentials, temperature spikes), invokes time-series models via Hugging Face API, and triggers emergency automated valve isolation when anomalies breach sovereign threshold standards.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a pipeline SCADA anomaly detection and action agent using Google ADK and Hugging Face Inference API.
How work is evaluated
Evaluates pipeline anomaly detection speed and accuracy from telemetry logs.
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.
detect_sudden_leak
Ensure pressure drop correctly triggers leak alarm.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
anomaly_precision
Precision of leak detection against baseline noise. • target: 0.96 • 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
Build pipeline telemetry processing agents using Google ADK.
Integrate Hugging Face Inference API for custom ML classification models.
Execute automated control tool calls upon anomaly confirmation.
Reference links and supporting material
Time-series pipeline telemetry dataset covering normal operation and leak anomaly scenarios.
Challenge setup
Evaluates pipeline anomaly detection speed and accuracy from telemetry logs.
Challenge input
JSON containing pipeline_section, pressure_psi_stream, flow_bph_stream
Google ADK
Google's native agent SDK for building task-oriented systems.
Hugging Face Inference API
Direct API access to fine-tuned time-series ML models.
Evaluated output
JSON with anomaly_detected, anomaly_type, isolation_recommended
- Ensure pressure drop correctly triggers leak alarm.
- Precision of leak detection against baseline noise. • target: 0.96 • range: 0-1
- Benchmark: IoT-Anomaly-Bench
- Anomaly Precision target: 0.96
- 1 public reference case
- Python execution harness
- Python 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.
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Configured tools
- Google · Required
- Hugging Face · Optional
- Google ADK · Optional
Evaluation contract
- detect_sudden_leak · Weight 1
- anomaly_precision · Weight 1
Metrics
- anomaly_precision · Target 0.96
Benchmarks
- IoT-Anomaly-Bench
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing pipeline_section, pressure_psi_stream, flow_bph_stream
Output: JSON with anomaly_detected, anomaly_type, isolation_recommended
Execution harness: Python
Evaluation tools
- Google ADK · Agent Architecture
- Hugging Face Inference API · Model Runtime
Recipe freeze marker
No recipe freeze time is recorded.