Data Science AI Agents

Production-grade Multi-Agent Data Science Orchestrator. Manages the complete ML pipeline — profiling, cleaning, EDA, feature engineering, model training, evaluation, and critic validation — via 7 specialized sub-agents. Supports classification, regression, clustering, time-series, and anomaly detection. Runs independent tasks in parallel, handles failures with intelligent retry, and never surfaces unvalidated results.

Analyzes raw datasets end-to-end: schema detection, row/column counts, data types, missing value rates, unique value analysis, statistical summaries (mean/median/std/skew/kurtosis), target-column candidate detection, and memory footprint. Outputs a structured profile artifact used by downstream DS agents.

Performs comprehensive Exploratory Data Analysis: descriptive statistics, distribution analysis (normality, skewness), correlation matrix, feature-target relationships, multicollinearity detection, anomaly and pattern discovery, and actionable visualization recommendations.

Trains multiple ML models for classification, regression, clustering, and time-series forecasting. Runs stratified train/test splits, cross-validation, hyperparameter tuning (RandomizedSearchCV), and returns training metrics, predictions, and saved model artifacts for all candidates.

Production-grade data cleaning agent for ML pipelines. Handles missing values, duplicate records, outlier detection and treatment, data type correction, inconsistent category normalization, and optional normalization/standardization. Uses the profile report from DS Data Profiler to make informed decisions.

Intelligent feature engineering agent for ML pipelines. Creates new features, applies transformations (log, polynomial, datetime decomposition), encodes categorical variables (OHE, target, ordinal), scales numerics, performs feature selection (RFECV, mutual info, VIF pruning), reduces dimensionality (PCA), and detects data leakage.

Comprehensive model evaluation agent. Computes full metric suites, runs nested cross-validation, detects overfitting and underfitting, performs calibration analysis for classifiers, checks generalization through learning curves, and compares all candidate models with statistical significance testing.

Independent critic and validation agent for the Data Science pipeline. Reviews all agent outputs for correctness, methodology soundness, data leakage, suspicious metrics, target contamination, and methodology errors. Returns PASS / FAIL / CONDITIONAL_PASS with detailed reasons and corrective recommendations. The final gatekeeper before results are presented to the user.