Automated Lab QC & Batch-Effect Agent System using AutoGen
Biomedical nucleic acid measurement instruments generate high-throughput quality control metrics vulnerable to batch effects. Developers will build an AutoGen multi-agent system where automated diagnostic agents collaborate to parse instrument logs, flag batch anomalies, and recommend lab rerun protocols with 98% pass/fail classification precision.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an AutoGen multi-agent conversational system to analyze nucleic acid measurement instrument quality logs and isolate batch effects.
How work is evaluated
Evaluates batch effect detection accuracy, agent agreement convergence speed, and rerun recommendation validity.
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.
batch_anomaly_detected_test
Verifies that low purity wells are correctly flagged.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
batch_detection_precision
Precision in identifying sub-standard sample wells. • target: 0.98 • 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
Set up AutoGen ConversableAgent instances for QC Inspection and Lab Operations.
Parse nucleic acid concentration, purity (A260/A280), and RIN scores from instrument outputs.
Detect systematic batch effects across multi-well plate layouts.
Automate action recommendations for sample reruns or calibration resets.
Reference links and supporting material
Microplate reader raw telemetry files including A260/A280, A260/A230, and concentration metrics across multiple runs.
Challenge setup
Evaluates batch effect detection accuracy, agent agreement convergence speed, and rerun recommendation validity.
Challenge input
JSON containing plate ID and per-well purity readings.
AutoGen
Facilitates multi-agent peer review conversations for anomaly resolution.
Evaluated output
JSON containing batch anomaly detected, affected wells, and rerun action.
- Verifies that low purity wells are correctly flagged.
- Precision in identifying sub-standard sample wells. • target: 0.98 • range: 0-1
- Benchmark: Lab Anomaly Detection Benchmark
- Batch Detection Precision target: 0.98
- 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.
View technical recipe
Configured tools
- AutoGen · Required
- Kombai · Optional
- Claude 4 Opus · Optional
Evaluation contract
- batch_anomaly_detected_test · Weight 1
- batch_detection_precision · Weight 1
Metrics
- batch_detection_precision · Target 0.98
Benchmarks
- Lab Anomaly Detection Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing plate ID and per-well purity readings.
Output: JSON containing batch anomaly detected, affected wells, and rerun action.
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Dialogue Framework
Recipe freeze marker
No recipe freeze time is recorded.