Idle Inventory Recovery Agent
Deploys a ReAct (Reasoning-Action-Observation) agent architecture that iteratively analyzes each part, evaluates recovery strategies using Multi-Criteria Decision Analysis, calculates optimal pricing using demand elasticity models, and generates actionable recovery plans. Includes predictive analytics, market intelligence integration, sustainability metrics, and comprehensive guardrails for safe autonomous operation.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agent Configuration for Idle Inventory Recovery with data source options, human-in-the-loop confidence thresholds, and 6-agent pool architecture.
Agents Working view showing ReAct reasoning loop with chain-of-thought processing, execution pipeline stages, tool invocations, and agent memory.
Recovery Analysis Results displaying projected recovery value, financial impact breakdown, strategy allocation chart, and AI-generated recommendations.
Audit Trail & Observability showing 65 reasoning steps, 16 tool invocations, 5 decisions made, with detailed event timeline and agent traces.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Multi-agent recovery workflows require centralized coordination, execution phase management, and graceful error handling across autonomous agents.
Core Logic
Coordinates the six-agent workflow through eight distinct execution phases. Manages agent activation, monitors progress metrics, handles execution state transitions, and maintains audit logging. Implements configurable retry policies, timeout management, and execution cancellation support. Provides real-time progress tracking and phase-level insights.
Data Analyzer Agent
Raw inventory exports require profiling, validation, aging distribution analysis, and ABC classification before recovery strategies can be determined.
Core Logic
Ingests inventory data from multiple sources (DMS integration, file upload, sample data). Executes aging pattern analysis with configurable thresholds, ABC classification using value-based Pareto analysis, and data quality scoring. Identifies critical aging items requiring immediate attention and generates data profiling reports with anomaly detection.
Strategy Agent
Each aging part requires evaluation across multiple recovery strategies (OEM Return, Dynamic Markdown, B2B Liquidation, Cross-Location Transfer) with complex trade-offs between recovery value, probability of success, speed, and effort.
Core Logic
Implements Multi-Criteria Decision Analysis (MCDA) with weighted scoring across five dimensions: Recovery Value (35%), Success Probability (30%), Speed (20%), Effort (10%), and Risk (5%). Evaluates each strategy option per part, generates confidence-scored recommendations, and routes low-confidence decisions to human review queue. Provides detailed reasoning traces for strategy selection.
Dynamic Pricing Agent
Static markdown approaches either leave money on the table or fail to clear inventory. Optimal pricing requires demand elasticity modeling and dynamic adjustment based on market conditions.
Core Logic
Calculates optimal pricing using demand elasticity models calibrated for automotive parts (coefficient range -1.2 to -1.8). Generates tiered markdown recommendations based on aging duration, computes expected demand increases at each price point, and projects recovery values. Integrates demand forecasting using exponential smoothing for forward-looking pricing decisions.
Execution Agent
Recovery strategies must be translated into actionable execution plans including OEM submission packages, markdown schedules, and B2B listing configurations.
Core Logic
Generates executable recovery plans with specific timelines and action items. Validates OEM return eligibility against program rules (return windows, packaging requirements, authorization status). Prepares OEM submission packages with required documentation. Creates phased execution schedules with resource allocation recommendations.
Validation Agent
AI-generated recommendations require validation against business rules, compliance requirements, and confidence thresholds before execution. Low-confidence decisions need human expert review.
Core Logic
Performs final validation including compliance checks (OEM return windows, pricing margin thresholds, inventory accuracy), confidence assessment against configurable thresholds, and human-in-the-loop routing for decisions below confidence threshold. Manages approval queue with priority ranking, generates executive summaries, and computes aggregate metrics for decision quality assessment.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization