AI Inventory Intelligence Worker
Deploys a coordinated multi-agent AI system that combines RAG-based knowledge retrieval, transparent reasoning traces, and human-in-the-loop validation. The system provides explainable AI decisions with confidence scoring, allowing parts professionals to understand exactly why recommendations are made while maintaining full control over final decisions.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Configure AI Agentic Workflow with data source connections, LLM model parameters (temperature, max tokens), and agent system orchestration settings.
Agent Orchestration Control Room displaying real-time multi-agent monitoring, progress tracking across 7 specialized agents, and live activity feed.
Workflow Results Dashboard showing executive summary with items processed/approved/rejected, financial impact analysis, ROI metrics, and compliance status.
Observability & Audit Trail with system performance metrics, workflow/agent/LLM inference statistics, RAG pipeline details, and complete audit log.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex multi-agent workflows require coordination, task distribution, progress monitoring, and error recovery across multiple specialized agents operating on shared data.
Core Logic
Serves as the central coordinator for the multi-agent system, managing execution plans with support for sequential, parallel, hierarchical, or swarm orchestration patterns. Monitors agent status, handles inter-agent communication, distributes tasks based on agent capabilities, and implements retry policies for fault tolerance. Maintains workflow state and coordinates handoffs between agents.
Data Analysis Agent
Raw inventory data requires validation, cleaning, pattern detection, ABC classification, and aging analysis before meaningful insights can be derived.
Core Logic
Processes incoming inventory data through schema validation, anomaly detection, and statistical analysis. Performs ABC classification using Pareto analysis, identifies aging patterns across multiple thresholds (90, 180, 365 days), calculates sales velocity metrics, and generates data quality scores. Outputs cleaned datasets with enriched feature engineering for downstream agents.
Market Intelligence Agent
Inventory decisions made in isolation miss market context including competitor pricing, demand trends, and supply chain conditions that significantly impact optimal strategies.
Core Logic
Aggregates external market data including competitor pricing analysis, demand signals from leading/lagging indicators, macroeconomic factors, and supply market conditions. Computes demand scores, price positioning analysis, and market sentiment indicators. Provides market-aware context to enhance decision accuracy.
Financial Modeling Agent
Inventory decisions require sophisticated financial analysis including ROI projections, carrying cost calculations, and working capital impact that manual processes struggle to compute at scale.
Core Logic
Executes financial models including Economic Order Quantity (EOQ) calculations, Gross Margin Return on Investment (GMROI), carrying cost analysis, and cash flow projections. Computes recovery value estimates, markdown optimization curves, and opportunity cost assessments. Generates financial impact summaries with confidence intervals.
Compliance & Risk Agent
Inventory decisions must comply with OEM program rules, regulatory requirements, and internal policies while managing financial and operational risks.
Core Logic
Validates recommendations against OEM return program eligibility criteria, regulatory compliance requirements, and configurable business rules. Performs risk scoring across multiple dimensions including stockout probability, supplier concentration, and policy exceptions. Generates compliance status reports and flags items requiring escalation.
Decision Synthesis Agent
Individual agent analyses must be synthesized into coherent, actionable recommendations with clear prioritization and expected outcomes.
Core Logic
Aggregates outputs from all specialized agents using Multi-Criteria Decision Analysis (MCDA) with configurable weighting. Generates prioritized recommendations with confidence scores, implementation steps, and projected impact metrics. Creates executive summaries, strategic recommendations, and action plans with dependency mapping.
Knowledge Retrieval Agent (RAG)
AI decisions lack access to institutional knowledge, historical precedents, policy documents, and domain expertise that human experts rely on.
Core Logic
Implements Retrieval Augmented Generation using vector similarity search across knowledge bases including policy documents, historical decisions, market data archives, and compliance guidelines. Retrieves relevant context with configurable top-K results and re-ranking. Provides source attribution with highlighted relevant passages for full transparency.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization