Supply Chain Intelligence Agent
Deploys an eight-agent orchestration system with DAG-based execution planning that provides comprehensive supply chain intelligence. Combines demand forecasting with ensemble ML models, multi-echelon inventory optimization, risk assessment with Monte Carlo simulation, and real-time market intelligence.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agent Configuration wizard for inventory optimization with data source selection including sample dataset (12,500 records), CSV upload, and API/database connections.
AI Agent Orchestration showing live pipeline progress at 58%, agent execution status, system resource metrics, and real-time reasoning trace with EOQ calculations.
Tool Executions view with 10/10 algorithms completed including DataValidator, FeatureEngineer, IsolationForest, ProphetModel, XGBoostEnsemble, and MonteCarloSimulator.
Review Configuration & Launch summary showing estimated resources (27s execution, 488MB memory), data source details, and business context for manufacturing use case.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex analytical workflows require intelligent planning, resource allocation, and coordination across multiple specialized agents with interdependencies.
Core Logic
Creates DAG-based execution plans identifying critical paths and parallelizable agent groups. Manages workflow coordination, agent activation sequencing, and resource allocation. Monitors session metrics including completion rates, execution times, and parallelization efficiency. Handles session lifecycle from configuration through completion with support for cancellation.
Data Processing Agent
Raw supply chain data requires rigorous validation, cleaning, and feature engineering before analysis. Data quality issues propagate errors through downstream models.
Core Logic
Executes comprehensive data pipeline including schema validation, null handling, duplicate detection, and outlier identification. Performs feature engineering generating derived variables (lag features, moving averages, seasonality indices). Produces data quality reports with field-level statistics and generates cleaned datasets with quality scores for downstream processing.
Pattern Analysis Agent
Supply chain data contains hidden patterns including seasonality, trends, anomalies, and correlations that manual analysis cannot systematically detect across thousands of SKUs.
Core Logic
Applies statistical techniques including Fourier transforms for seasonal component detection, Isolation Forest for anomaly identification, and cross-correlation analysis for feature relationships. Detects weekly, monthly, and quarterly patterns. Generates pattern reports with confidence scores and flags anomalous data points requiring investigation.
Forecasting Agent
Accurate demand forecasting requires sophisticated modeling beyond simple extrapolation. Single-model approaches fail to capture the complexity of real-world demand patterns.
Core Logic
Implements ensemble forecasting combining ARIMA for time-series patterns, Prophet for seasonality and trend decomposition, and XGBoost for non-linear relationships. Automatically selects optimal models per SKU based on cross-validation MAPE. Generates confidence intervals at 95% level and provides forecast accuracy metrics with feature importance rankings.
Optimization Agent
Inventory optimization involves complex trade-offs between service levels, carrying costs, ordering costs, and working capital that require mathematical optimization approaches.
Core Logic
Formulates and solves multi-objective optimization problems including Economic Order Quantity (EOQ) calculations, safety stock optimization based on service level targets, and multi-echelon inventory optimization. Computes optimal order quantities, reorder points, and timing. Reports convergence metrics, objective function improvements, and projected cost savings.
Risk Assessment Agent
Supply chains face multiple risk categories (stockout, supplier concentration, demand forecast error, lead time variability) requiring systematic assessment and mitigation planning.
Core Logic
Executes comprehensive risk assessment using Monte Carlo simulation for stockout probability modeling, supplier reliability scoring, and geopolitical risk evaluation. Calculates composite risk scores with category breakdowns. Generates prioritized mitigation strategies with effectiveness estimates and implementation timelines. Provides monitoring recommendations for ongoing risk management.
Recommendation Agent
Analytical outputs must be synthesized into prioritized, actionable recommendations with clear business impact quantification and implementation guidance.
Core Logic
Aggregates outputs from all upstream agents and generates prioritized recommendations using multi-criteria ranking (impact, feasibility, urgency). Each recommendation includes detailed rationale, supporting evidence with confidence scores, estimated ROI, implementation steps, dependencies, risks, and timeframes. Produces executive-ready action plans with owner assignments.
Explainability Agent
AI recommendations require transparency for stakeholder trust, regulatory compliance, and continuous improvement. Black-box outputs undermine adoption and accountability.
Core Logic
Generates comprehensive explainability reports including decision tree visualizations, SHAP-based feature importance rankings, confidence breakdowns by component, and complete traceability records linking outputs to source data and transformations. Creates human-readable explanations suitable for both executive and technical audiences. Ensures all recommendations are auditable and defensible.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization