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Digital workers, evaluated and owned

Prioritize the right processes. Measure the results. Keep the source code.

Digital Worker 8 AI Agents Example

Supply Chain Intelligence Agent

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.

8 AI Agents
4 Tech Stack
AI Orchestrated
Scoped Availability
Worker ID: supply-chain-intelligence-agent

Problem Statement

The challenge addressed

Modern supply chains face growing complexity including demand volatility, supplier concentration risks, ESG compliance requirements, and geopolitical disruptions. Traditional planning tools lack the analytical depth and forward-looking intelligence r...

Solution Architecture

AI orchestration approach

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Deploys an eight-agent orchestration system with DAG-based execution planning that provides comprehensive supply chain int...
Interface Preview 4 screenshots

Illustrative preview (sample data, not verified outcomes): AI Agent Configuration wizard for inventory optimization with data source selection including sample dataset (12,500 records), CSV upload, and API/database connections.

Illustrative preview (sample data, not verified outcomes): AI Agent Orchestration showing live pipeline progress at 58%, agent execution status, system resource metrics, and real-time reasoning trace with EOQ calculations.

Illustrative preview (sample data, not verified outcomes): Tool Executions view with 10/10 algorithms completed including DataValidator, FeatureEngineer, IsolationForest, ProphetModel, XGBoostEnsemble, and MonteCarloSimulator.

Illustrative preview (sample data, not verified outcomes): Review Configuration & Launch summary showing estimated resources (27s execution, 488MB memory), data source details, and business context for manufacturing use case.

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

8 Agents
Parallel Execution
AI Agent

Orchestrator Agent

Complex analytical workflows require intelligent planning, resource allocation, and coordination across multiple specialized agents with interdependencies.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #1
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #2
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #3
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. 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.

EXAMPLE #4
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #5
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AI Agent

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

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #6
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AI Agent

Recommendation Agent

Analytical outputs must be synthesized into prioritized, actionable recommendations with clear business impact quantification and implementation guidance.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #7
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AI Agent

Explainability Agent

AI recommendations require transparency for stakeholder trust, regulatory compliance, and continuous improvement. Black-box outputs undermine adoption and accountability.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. 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.

EXAMPLE #8
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

The Supply Chain Intelligence Agent is an enterprise AI orchestration platform designed for comprehensive inventory and supply chain optimization. It features eight specialized agents executing through seven coordinated phases: Initialization, Data Processing, Pattern Analysis, Forecasting, Optimization, Risk Assessment, and Synthesis. The system generates multi-dimensional insights including predictive demand forecasting, supply chain resilience analysis, ESG sustainability metrics, market intelligence, and actionable recommendations. All outputs include explainability reports with decision trees, feature importance rankings, and complete traceability from input data to final recommendations.

Tech Stack

4 technologies

Time-series forecasting models (ARIMA, Prophet, XGBoost ensemble)

Monte Carlo simulation engine for risk scenario analysis

ESG scoring framework aligned with GRI and SASB standards

Real-time market data feeds and commodity price integration

Architecture Diagram

System flow visualization

Supply Chain Intelligence Agent Architecture
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