--- title: "Building a Product Discovery Agent: AI-Powered Shopping Assistance and Recommendations" description: "Learn how to build an AI agent that helps shoppers discover products through natural language search, personalized filtering, side-by-side comparisons, and recommendation engines tailored to individual preferences." canonical: https://callsphere.ai/blog/building-product-discovery-agent-ai-shopping-assistance-recommendations category: "Learn Agentic AI" tags: ["Product Discovery", "E-Commerce AI", "Recommendation Engine", "Retail AI", "Shopping Assistant"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-07T12:41:41.955Z --- # Building a Product Discovery Agent: AI-Powered Shopping Assistance and Recommendations > Learn how to build an AI agent that helps shoppers discover products through natural language search, personalized filtering, side-by-side comparisons, and recommendation engines tailored to individual preferences. ## Why Product Discovery Matters in E-Commerce The average online store carries thousands of SKUs. Customers who cannot find what they want within a few interactions leave — and most never return. Traditional keyword search misses intent. A customer typing "something warm for a mountain trip" gets zero results on a site that indexes by material and garment type. An AI-powered product discovery agent bridges this gap by understanding natural language queries, mapping them to product attributes, and personalizing results based on browsing history, past purchases, and stated preferences. ## Architecture of a Product Discovery Agent The agent sits between the customer and your product catalog. It receives free-text queries, extracts structured intent, queries your catalog database, applies personalization, and returns ranked results. The core loop involves three tools: semantic search, attribute filtering, and comparison. ```mermaid flowchart LR CALLER(["Shopper"]) subgraph TEL["Telephony"] SIP["Twilio SIP and PSTN"] end subgraph BRAIN["E-commerce AI Agent"] STT["Streaming STT Deepgram or Whisper"] NLU{"Intent and Entity Extraction"} TOOLS["Tool Calls"] TTS["Streaming TTS ElevenLabs or Rime"] end subgraph DATA["Live Data Plane"] CRM[("CRM and Notes")] CAL[("Calendar and Schedule")] KB[("Knowledge Base and Policies")] end subgraph OUT["Outcomes"] O1(["Order status answered"]) O2(["Return RMA created"]) O3(["Specialist handoff"]) end CALLER --> SIP --> STT --> NLU NLU -->|Lookup| TOOLS TOOLS CRM TOOLS CAL TOOLS KB NLU --> TTS --> SIP --> CALLER NLU -->|Resolved| O1 NLU -->|Schedule| O2 NLU -->|Escalate| O3 style CALLER fill:#f1f5f9,stroke:#64748b,color:#0f172a style NLU fill:#4f46e5,stroke:#4338ca,color:#fff style O1 fill:#059669,stroke:#047857,color:#fff style O2 fill:#0ea5e9,stroke:#0369a1,color:#fff style O3 fill:#f59e0b,stroke:#d97706,color:#1f2937 ``` ```python from agents import Agent, Runner, function_tool from dataclasses import dataclass from typing import Optional @dataclass class ProductResult: id: str name: str price: float category: str attributes: dict score: float # Simulated catalog with embeddings PRODUCT_CATALOG = [ {"id": "SKU-001", "name": "Merino Wool Hiking Jacket", "price": 189.99, "category": "Outerwear", "attributes": {"warmth": "high", "activity": "hiking", "material": "merino"}}, {"id": "SKU-002", "name": "Down Insulated Parka", "price": 249.99, "category": "Outerwear", "attributes": {"warmth": "extreme", "activity": "general", "material": "down"}}, {"id": "SKU-003", "name": "Fleece Midlayer Pullover", "price": 79.99, "category": "Midlayers", "attributes": {"warmth": "medium", "activity": "hiking", "material": "fleece"}}, ] @function_tool def search_products(query: str, max_results: int = 5) -> str: """Search the product catalog using natural language.""" query_lower = query.lower() scored = [] for product in PRODUCT_CATALOG: score = 0 searchable = f"{product['name']} {product['category']} " \ f"{' '.join(product['attributes'].values())}".lower() for word in query_lower.split(): if word in searchable: score += 1 if score > 0: scored.append({**product, "relevance_score": score}) scored.sort(key=lambda x: x["relevance_score"], reverse=True) return str(scored[:max_results]) @function_tool def filter_by_price(min_price: float, max_price: float) -> str: """Filter products within a price range.""" results = [p for p in PRODUCT_CATALOG if min_price str: """Compare products side by side. Accepts comma-separated IDs.""" ids = [pid.strip() for pid in product_ids.split(",")] matches = [p for p in PRODUCT_CATALOG if p["id"] in ids] if len(matches) str: """Fetch user history and build context string.""" # In production, query your user profile database user_profile = { "preferred_brands": ["Patagonia", "Arc'teryx"], "size": "M", "past_categories": ["Outerwear", "Footwear"], "price_sensitivity": "moderate", "climate": "cold", } return ( f"Customer preferences: prefers {', '.join(user_profile['preferred_brands'])}. " f"Size {user_profile['size']}. Usually buys {', '.join(user_profile['past_categories'])}. " f"Price sensitivity: {user_profile['price_sensitivity']}. " f"Lives in a {user_profile['climate']} climate." ) # Inject personalization into the agent at runtime personalized_agent = Agent( name="Personalized Discovery Assistant", instructions=f"""You are a shopping assistant with knowledge of this customer. {build_personalization_context("user-123")} Use these preferences to rank and explain recommendations.""", tools=[search_products, filter_by_price, compare_products], ) ``` ## Handling Edge Cases Production discovery agents must handle empty results gracefully. When no products match, the agent should suggest broadening the search, offer related categories, or ask the customer to adjust their criteria rather than returning a blank response. ```python @function_tool def suggest_alternatives(category: str) -> str: """Suggest alternative categories when no exact match is found.""" alternatives_map = { "Outerwear": ["Midlayers", "Vests", "Rain Shells"], "Footwear": ["Socks", "Insoles", "Gaiters"], "Midlayers": ["Base Layers", "Outerwear", "Vests"], } suggestions = alternatives_map.get(category, ["Popular Items"]) return f"No exact matches. Try browsing: {', '.join(suggestions)}" ``` ## FAQ ### How do I integrate vector search for semantic product discovery? Use an embedding model like OpenAI `text-embedding-3-small` to encode product descriptions into vectors. Store these in a vector database such as Pinecone, Weaviate, or pgvector. At query time, embed the customer query and perform a cosine similarity search against product vectors. This allows the agent to find "warm jacket for mountains" even if no product contains those exact words. ### How should I handle product availability in real time? Add an inventory check tool that queries your inventory management system before presenting results. Filter out-of-stock items by default but offer a "notify me when back in stock" option. Cache inventory status with a short TTL (30-60 seconds) to avoid hammering your inventory API on every search. ### What metrics should I track for a product discovery agent? Track search-to-cart conversion rate, zero-result rate, average number of agent turns before a product is selected, and click-through rate on recommended items. Compare these against your baseline keyword search. A well-built discovery agent should reduce zero-result rates by 40-60 percent and increase add-to-cart rates measurably. --- #ProductDiscovery #ECommerceAI #RecommendationEngine #RetailAI #ShoppingAssistant #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/building-product-discovery-agent-ai-shopping-assistance-recommendations