Retail AI Agents & Tools Overview¶

The Retail AI system is built around specialized AI agents that orchestrate multiple tools to handle complex retail operations. This guide explains how to build and use these agents for retail customer assistance, inventory management, and product discovery.

What are Retail AI Agents?¶

Retail AI agents are autonomous, conversational AI systems that: - Reason and plan multi-step workflows to solve customer problems - Use specialized tools for data access, search, and analysis - Maintain conversation context across multiple interactions - Apply business rules and safety guardrails - Specialize in retail domains like products, inventory, and customer service

Agent Architecture¶

graph TD
    A[Customer Query] --> B[Router Agent]
    B --> C{Query Type?}

    C -->|Product Info| D[Product Agent]
    C -->|Stock Check| E[Inventory Agent]
    C -->|Compare Items| F[Comparison Agent]
    C -->|DIY Help| G[DIY Agent]
    C -->|General| H[General Agent]

    D --> I[Unity Catalog Tools]
    D --> J[Vector Search Tools]
    E --> I
    E --> J
    F --> I
    F --> K[LangChain Tools]
    G --> L[Web Search Tools]
    G --> J
    H --> J

    I --> M[Response]
    J --> M
    K --> M
    L --> M

    style D fill:#e1f5fe
    style E fill:#f3e5f5
    style F fill:#e8f5e8
    style G fill:#fff3e0
    style H fill:#fce4ec

Implemented Agents Summary¶

Product Agent¶

Specialization: Product discovery, details, and recommendations - Exact product lookup by SKU or UPC - Semantic product search using natural language - Product feature extraction and analysis - Cross-selling and upselling recommendations

Inventory Agent¶

Specialization: Stock levels, availability, and inventory management - Real-time inventory checking across stores and warehouses - Stock level monitoring and alerts - Product availability by location - Inventory movement tracking

Comparison Agent¶

Specialization: Product comparisons and recommendation analysis - Side-by-side product comparisons - Feature analysis and scoring - Pros and cons evaluation - Personalized recommendations based on user needs

DIY Agent¶

Specialization: Project guidance, tutorials, and general assistance - Web search for external information - General product discovery - Open-ended customer assistance - Fallback for queries outside other agent specializations

General Agent¶

Specialization: Store policies, hours, and general customer service - Store information and policies - General customer service inquiries - Fallback for unrouted queries - Policy and procedure guidance

Recommendation Agent¶

Specialization: Product recommendations and suggestions - Personalized product recommendations - Cross-selling and upselling - Product discovery based on preferences - Recommendation explanations

For detailed agent specifications, configurations, and examples, see Agent Reference

Tools Overview¶

Agents use different types of tools depending on their needs. Here's an overview of available tool categories:

Unity Catalog Functions¶

High-performance SQL functions for exact data lookups

Best for: Known SKUs/UPCs, real-time inventory checks, exact product matches

find_product_by_sku - Product details by SKU
find_product_by_upc - Product details by UPC
find_inventory_by_sku - Global inventory levels
find_store_inventory_by_sku - Store-specific inventory

Performance: ~200ms average latency, 99.5% success rate

Vector Search Tools¶

Semantic search for natural language queries

Best for: Natural language product discovery, "find similar" queries, content search

Semantic product search using embeddings
Natural language query understanding
Similarity-based product recommendations

Performance: ~300ms average latency, 98.8% success rate

LangChain Tools¶

AI-powered analysis and processing tools

Best for: Product comparison, text extraction, classification, complex analysis

product_comparison - Detailed product analysis
sku_extraction - Extract SKUs from text
product_classification - Categorize products

Performance: ~1.5s average latency, 97.9% success rate

External Tools¶

Web search and external data sources

Best for: Real-time information, tutorials, general knowledge, trending topics

Web search capabilities
External API integrations
Real-time data access

Performance: ~2.0s average latency, 96.5% success rate

For complete tool specifications, input/output examples, and implementation details, see Tools Reference

Best Practices¶

Key Guidelines¶

Single Responsibility - Each agent should have a focused purpose
Fail Gracefully - Always provide meaningful fallbacks
Context Awareness - Leverage available context for better responses
Performance First - Choose tools based on performance requirements
Safety & Compliance - Implement comprehensive guardrails

Development Principles¶

Clear role definition in prompts
Structured response formats
Comprehensive error handling
Performance monitoring
Gradual deployment strategies

For comprehensive best practices, safety guidelines, and deployment strategies, see Agent Best Practices

Troubleshooting¶

Common Issues¶

Agent Not Responding - Check configuration, model endpoints, and tool availability
Tool Errors - Verify authentication, permissions, and data access
Performance Issues - Analyze response times, optimize tool selection
Authentication Problems - Validate Databricks credentials and permissions

Quick Diagnostics¶

# Test agent configuration
diagnose_agent_config(agent_name, model_config)

# Test tool availability
test_tools(agent_tools)

# Check authentication
diagnose_databricks_auth()

For comprehensive troubleshooting guides, diagnostic tools, and solutions, see Agent Troubleshooting

Agent Documentation¶

Agent Reference - Detailed specifications for all implemented agents
Agent Quickstart - Quick guide to building and optimizing agents
Agent Development Patterns - Common patterns and implementation techniques
Agent Performance - Performance metrics, optimization, and monitoring
Agent Best Practices - Guidelines for development and deployment
Agent Troubleshooting - Debugging and problem resolution

Tool Documentation¶

Tools Reference - Complete technical specifications for all tools
AI Agent Implementation - Advanced agent patterns

Development Resources¶

Development Guide - Building custom agents and tools
Architecture Overview - System design and patterns
Deployment Guide - Production deployment strategies

Next Steps¶

Explore Agent Capabilities - Review the Agent Reference to understand what each agent can do
Quick Start Building - Follow the Agent Quickstart to build your first agent
Understand Tools - Check the Tools Reference for implementation details
Learn Patterns - Study Development Patterns for advanced techniques
Optimize Performance - Use Performance Guidelines to improve response times
Deploy Safely - Apply Best Practices for production deployment

Ready to build intelligent retail AI agents? Start with the Agent Quickstart for a hands-on guide!