---
title: "Supply Chain Optimization Agent"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Supply Chain Optimization Agent

Let’s create an example of a **Supply Chain Optimization Agent** for a logistics company. This agent will monitor inventory levels, optimize delivery routes, and notify managers for approval on critical decisions, while also allowing managers to manually request route adjustments with agent-verified optimizations. I’ll first outline the requirements and then detail the implementation using the same architecture: **event-driven architecture (EDA)**, **three-tier microservices architecture**, **stateless computing**, **scheduled computing (CronJobs)**, and **human-in-the-loop (HITL)**.

---

### Requirements for the Supply Chain Optimization Agent

#### Functional Requirements
1. **Inventory Monitoring and Alerts**:
   - Continuously monitor inventory levels across warehouses.
   - Detect low stock or overstock conditions and suggest restocking or redistribution actions.
   - Notify managers with suggested actions for approval.

2. **Route Optimization**:
   - Automatically optimize delivery routes based on real-time data (e.g., traffic, order volume).
   - Suggest optimized routes to drivers or fleet managers for approval before execution.

3. **Action Approval and Execution**:
   - Allow managers to approve, modify, or reject suggested inventory actions or routes.
   - Execute approved actions (e.g., dispatch trucks, order stock).

4. **Manual Route Adjustment**:
   - Enable managers to request custom route adjustments (e.g., prioritize a VIP customer).
   - Agent verifies and optimizes the request, suggesting improvements, and seeks approval before finalizing.

#### Non-Functional Requirements
1. **Scalability**: Handle multiple warehouses and delivery fleets.
2. **Real-Time**: Provide timely alerts and route updates.
3. **Reliability**: Ensure accurate inventory tracking and route calculations.
4. **Usability**: Offer a clear interface for managers to review and approve actions.
5. **Adaptability**: Learn from manager feedback to improve suggestions.

#### User Stories
- As a manager, I want to be alerted about low inventory with restocking suggestions so I can maintain stock levels.
- As a manager, I want optimized delivery routes suggested to me for approval to ensure efficient logistics.
- As a manager, I want to request custom route changes, reviewed by the agent, to meet specific business needs.

---

### Implementation Using the Defined Architecture

#### Architecture Overview
- **Three-Tier**: Presentation (UI for managers), Business Logic (agent processing), Data (inventory and route data).
- **EDA**: Events drive inventory checks, route optimization, and approvals.
- **Stateless Computing**: Scalable processing of inventory and route tasks.
- **CronJobs**: Periodic inventory audits and data updates.
- **HITL**: Managers approve critical actions and custom requests.

---

#### Components and Workflow

##### 1. Three-Tier Architecture
- **Presentation Layer**:
  - A web dashboard or mobile app where managers:
    - View inventory alerts and suggested actions (e.g., "Restock Warehouse A").
    - Review and approve optimized routes.
    - Submit custom route adjustment requests.
  - Notifications (e.g., email, SMS) for urgent alerts.
- **Business Logic Layer**:
  - **Inventory Agent**: Monitors stock levels, detects anomalies, and suggests actions.
  - **Route Optimizer Agent**: Calculates optimal delivery routes using real-time data.
  - **HITL Coordinator**: Manages human approval workflows for inventory actions and routes.
- **Data Layer**:
  - Stores:
    - Inventory levels (e.g., product ID, quantity, warehouse).
    - Delivery data (e.g., orders, truck locations, traffic conditions).
    - Suggested actions/routes and their approval status.
  - Tools: Database (e.g., MongoDB), cache (e.g., Redis) for real-time access.

##### 2. Event-Driven Architecture
- **Event Types**:
  - `InventoryUpdate`: Triggered when stock levels change.
  - `InventoryActionSuggested`: Low/overstock detected with a suggested action.
  - `RouteOptimized`: New route calculated for a delivery.
  - `HumanReviewRequired`: Sent when manager approval is needed (for actions or routes).
  - `HumanResponseReceived`: Manager approves/modifies/rejects a suggestion.
  - `ActionExecuted`: Approved action or route is implemented.
- **Event Bus**: Use a message broker (e.g., Apache Kafka) to route events.
- **Workflow**:
  1. `InventoryUpdate` → Inventory Agent suggests restocking → `InventoryActionSuggested`.
  2. `RouteOptimized` → Route Optimizer suggests a new route → `HumanReviewRequired`.
  3. `HumanResponseReceived` → Action executed → `ActionExecuted`.

##### 3. Stateless Computing
- **Inventory Processor**: Stateless service (e.g., Kubernetes pod) that:
  - Consumes `InventoryUpdate`, checks thresholds, and emits `InventoryActionSuggested`.
  - Scales with warehouse count.
- **Route Optimizer**: Stateless function (e.g., AWS Lambda) calculating routes using algorithms (e.g., Dijkstra’s) or APIs (e.g., Google Maps).
- **HITL Handler**: Stateless service presenting tasks to managers and processing responses.
- **Action Executor**: Stateless function executing approved actions (e.g., sending restock orders, updating truck GPS).

##### 4. Scheduled Computing (CronJobs)
- **Inventory Auditor**: Runs hourly to sync inventory data from warehouses and emit `InventoryUpdate` if discrepancies are found.
- **Route Planner**: Daily job pre-calculates base routes for the next day’s deliveries, emitting `RouteOptimized` for review.

##### 5. Human-in-the-Loop (HITL)
- **Inventory Actions**:
  - After `InventoryActionSuggested` (e.g., "Order 100 units for Warehouse B"), HITL Handler pushes it to the dashboard.
  - Manager approves → `HumanResponseReceived` → Action executed.
- **Route Optimization**:
  - After `RouteOptimized` (e.g., "Route A: 3 stops, 2 hours"), HITL Handler seeks approval.
  - Manager approves → `HumanResponseReceived` → Route sent to drivers.
- **Manual Route Adjustment**:
  - Manager submits a custom route via UI → Route Optimizer verifies/optimizes → `HumanReviewRequired`.
  - Manager approves → `HumanResponseReceived` → Route executed.

---

#### Detailed Implementation

##### Step 1: Inventory Monitoring
- **Tech**: IoT sensors or warehouse API for real-time stock updates.
- **Flow**:
  - Sensor/API updates stock → `InventoryUpdate {warehouseId, productId, quantity}`.
  - Inventory Processor (stateless) checks thresholds (e.g., <10 units = low) → `InventoryActionSuggested {action: "Restock 50 units"}`.

##### Step 2: Route Optimization
- **Tech**: Route optimization algorithm or API in a stateless function.
- **Flow**:
  - New order or traffic update → Route Optimizer calculates → `RouteOptimized {routeId, stops, ETA}`.
  - Stored in data layer → `HumanReviewRequired`.

##### Step 3: HITL for Approvals
- **Tech**: Dashboard (Vue.js/Django) + HITL Handler (Lambda).
- **Flow**:
  - HITL Handler pushes `HumanReviewRequired` to UI (e.g., "Restock 50 units - Approve?").
  - Manager responds → `HumanResponseReceived {taskId, decision}`.
  - Action Executor implements (e.g., sends restock order via API).

##### Step 4: Manual Route Adjustment
- **Tech**: UI form + Route Optimizer.
- **Flow**:
  - Manager submits: "Prioritize Customer X" → Route Optimizer adjusts → `HumanReviewRequired {routeId, newRoute}`.
  - Manager approves → `HumanResponseReceived` → Route sent to driver.

##### Step 5: Data Management
- **Schema**:
  - `Inventory`: {warehouseId, productId, quantity, status}
  - `Routes`: {routeId, stops, ETA, status}
  - `HITL_Tasks`: {taskId, type: "inventory/route", suggestion, status}
- **Storage**: MongoDB for flexibility, Redis for pending tasks.

##### Step 6: Learning Loop
- CronJob aggregates `HumanResponseReceived` data → Refines optimization models (e.g., adjust restock thresholds) weekly.

---

#### Example Workflow
1. **Inventory Alert**:
   - Stock drops to 5 units → `InventoryUpdate` → `InventoryActionSuggested: "Restock 20 units"`.
   - Manager approves via dashboard → `HumanResponseReceived` → Restock ordered.
2. **Route Optimization**:
   - New order → `RouteOptimized: "Route B: 4 stops, 3 hours"`.
   - Manager approves → `HumanResponseReceived` → Driver dispatched.
3. **Manual Adjustment**:
   - Manager requests: "Add VIP stop" → Route Optimizer suggests → Manager approves → Route updated.

---

### Benefits
- **Real-Time**: EDA ensures instant inventory and route updates.
- **Scalable**: Stateless services handle multiple warehouses/fleets.
- **Manager Control**: HITL keeps humans in critical decision loops.
- **Proactive**: CronJobs maintain system accuracy.

### Challenges
- **Data Sync**: Real-time inventory updates require robust integrations.
- **Route Complexity**: Optimization might need advanced algorithms for large fleets.

This Supply Chain Optimization Agent leverages the architecture to balance automation and human oversight in a logistics domain.
