AI Supply Chain Command Center
Orchestrates 8 specialized AI agents that perform parallel analysis of disruption scenarios. Implements visible Chain-of-Thought reasoning so users can observe agent thinking processes, Inter-Agent Communication for collaborative analysis, Human-in-the-Loop approval workflows for recommendations, comprehensive audit trails for compliance, and autonomous actions for low-risk responses.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Active agents dashboard showing 8 specialized agents with orchestration metrics, chain-of-thought reasoning panel, and real-time agent communication logs
AI Recommendations panel with Human-in-the-Loop approval workflow displaying actionable recommendations with cost, time-to-resolve, and expected improvement metrics
Analysis complete summary showing autonomous resolution at 100% Auto mode, $250K revenue protected, stakeholder satisfaction at 94%, and risk mitigation outcomes
Complete audit trail with timestamped agent activities, workflow checkpoints, inter-agent messaging, and tool invocations for compliance documentation
AI Agents
Specialized autonomous agents working in coordination
Master Orchestrator Agent
Complex disruption scenarios require coordinated analysis across multiple specialist agents with proper task sequencing, priority management, and error handling.
Core Logic
Plans workflow execution based on scenario type and severity, delegates tasks to specialist agents in optimal sequence, manages agent dependencies and communication, handles escalation decisions, coordinates human-in-the-loop approvals, and synthesizes final recommendations from all agent outputs. Model: GPT-4 Turbo (1.7T parameters, MoE architecture).
Data Aggregation Agent
Supply chain analysis requires gathering data from multiple systems (ERP, WMS, TMS, quality systems) with different schemas, freshness levels, and access patterns.
Core Logic
Fetches data from multiple sources with appropriate latency handling, normalizes schemas for consistent analysis, implements real-time streaming for critical metrics, manages caching for frequently accessed data, and enriches data with historical context. Model: Claude 3 Haiku (20B parameters).
Root Cause Analyst Agent
Understanding disruption root causes requires pattern recognition across historical data, correlation analysis, anomaly detection, and connecting seemingly unrelated events.
Core Logic
Applies pattern recognition algorithms to identify recurring issues, performs correlation analysis between variables, detects anomalies in operational metrics, identifies root causes through 5-Why analysis methodology, compares current situation with historical incidents for insight. Model: Claude 3.5 Sonnet (175B parameters, 96% accuracy).
Impact Predictor Agent
Decision-makers need forecasts of how disruptions will impact delivery schedules, revenue, customer relationships, and operational metrics over different time horizons.
Core Logic
Executes demand forecasting models with confidence intervals, predicts business and operational impacts under different scenarios, generates scenario simulations with variable sensitivity, performs trend analysis on key metrics, provides probabilistic outcomes with uncertainty quantification. Model: Time Series Transformer - Chronos T5 Large (710M parameters).
Risk Assessment Agent
Disruption response requires comprehensive risk evaluation across regulatory, financial, operational, and reputational dimensions with quantified probabilities and impacts.
Core Logic
Identifies risks across multiple categories, estimates probabilities using historical data and current conditions, scores potential impacts on business metrics, generates mitigation strategies with resource requirements, validates compliance implications of response options. Model: Risk-GPT v2.3 (13B parameters, specialized for risk).
Solution Generator Agent
Generating actionable recommendations requires synthesizing analysis from multiple agents, considering constraints, evaluating trade-offs, and creating implementable action plans.
Core Logic
Synthesizes insights from all specialist agents into coherent recommendations, generates multiple solution options with trade-off analysis, optimizes recommendations against constraints (budget, time, capacity), creates detailed implementation plans with timelines and resource allocation, calculates ROI and payback periods. Model: GPT-4 Turbo.
Recommendation Validator Agent
Recommendations must be validated against company policies, regulatory requirements, operational feasibility, and safety standards before human review.
Core Logic
Validates recommendations against policy compliance rules, checks constraint feasibility (budget limits, capacity constraints, lead times), performs safety verification for operational changes, routes recommendations to appropriate human approvers based on impact level, provides pre-approval confidence scores. Model: Policy-Check-LLM v1.5 (7B parameters, 97% accuracy).
Communication Agent
Stakeholder communication during disruptions requires tailored messaging for different audiences (customers, suppliers, executives, operations) across multiple channels.
Core Logic
Drafts stakeholder communications with appropriate tone and detail level for each audience, adapts messaging for different channels (email, portal notification, executive brief), selects appropriate templates based on scenario type and severity, performs sentiment analysis on incoming communications, generates status update sequences. Model: Claude 3.5 Sonnet (175B parameters).
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization