Agentic Commerce & Shopping Agents

Agentic commerce refers to the use of autonomous AI agents that search, compare, negotiate, and purchase products on behalf of humans or businesses with minimal manual intervention. Unlike traditional ecommerce where a human browses a website, clicks add-to-cart, and completes checkout, agentic commerce delegates these steps to software agents that consume structured data feeds, make decisions based on rules or learned preferences, and execute transactions programmatically. This shift demands a fundamentally different data infrastructure — one optimized for machine speed and structured machine-readable formats rather than human-browsable web pages.

Our REST and GraphQL endpoints deliver median response times under 800ms for single-product lookups and under 1.5 seconds for multi-marketplace comparison queries that fan out to 10+ sources. WebSocket price streams deliver change events within 300-500ms of detection on the source marketplace. We maintain API endpoints in three regions (US East, EU West, APAC Singapore) so agents connect to the nearest point of presence. For latency-critical workflows, our edge cache layer serves frequently queried products in under 100ms.

Our entity resolution pipeline uses a three-tier matching strategy. First, exact identifier matching on UPC, GTIN, EAN, ISBN, and manufacturer part numbers catches approximately 70% of matches. Second, a Siamese neural network compares product attributes — title, brand, model, specifications, and images — to identify matches that lack shared identifiers, catching another 25%. Third, human-in-the-loop review handles the remaining ambiguous cases and feeds corrections back into the model. Every match includes a confidence score so agents can set their own threshold for what constitutes a verified match versus a probable match.

We support REST with JSON responses, GraphQL for flexible field selection, WebSocket for real-time streaming, and server-sent events (SSE) for one-way push notifications. All endpoints use strict JSON Schema validation with semantic versioning. We also provide OpenAI-compatible function-calling schemas so LLM-based agents can invoke our APIs as native tool calls, plus pre-built integrations for LangChain, CrewAI, and AutoGen. Bulk queries accept newline-delimited JSON (NDJSON) for efficient batch processing.

Freshness depends on the product category and volatility. High-volatility products like electronics, trending items, and deal-prone categories are re-crawled every 15-30 minutes. Standard catalog products are refreshed every 1-4 hours. When an agent queries a product, we return the most recent cached price along with a fetched_at timestamp and a freshness confidence score. If the cached data exceeds the agent{'"'}s freshness threshold, it can request an on-demand re-fetch that returns live data within 3-5 seconds. Price stream subscribers receive change events in near-real-time regardless of crawl schedule.

Yes. Every product response includes an availability object with stock status (in_stock, out_of_stock, limited_stock, pre_order, back_order), estimated quantity when available, warehouse or fulfillment center indicators, and estimated delivery windows. For agents executing autonomous purchases, we recommend a just-in-time inventory check immediately before checkout initiation — our on-demand check endpoint returns fresh availability in under 2 seconds. We also offer low-stock and back-in-stock webhook alerts so agents can act the moment inventory status changes.

Our rate limiting is designed for agent workloads, not human browsing patterns. Standard plans support 100 requests per second with burst allowance up to 200 rps. Enterprise plans offer dedicated capacity pools with guaranteed throughput up to 2,000 rps. All responses include standard rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset) so agents can self-throttle. Our SDKs include built-in exponential backoff and request queuing. For bulk operations like catalog syncs, we provide batch endpoints that accept up to 500 product IDs per request.

We cover 120+ marketplaces and retailers globally, including Amazon (all 20+ regional domains), Walmart, Target, Best Buy, Costco, Home Depot, eBay, Shopee, Lazada, Mercado Libre, Coupang, Rakuten, JD.com, and Alibaba. Coverage extends to vertical-specific platforms like Chewy, Sephora, Wayfair, and Zalando. Each marketplace is monitored for structural changes, and our extraction layer adapts automatically using the same AI-powered techniques described in our AI-powered data extraction solution. New marketplace integrations are typically completed within 5-7 business days upon request.

We provide first-class integrations for the major agent frameworks. For OpenAI-based agents, we publish function-calling JSON schemas that describe every API endpoint as a callable tool with typed parameters and return values. For LangChain, we offer a pip-installable tool wrapper that handles authentication, pagination, and error retry automatically. For CrewAI and AutoGen, we provide agent tool definitions that follow each framework{'"'}s conventions. All integrations include streaming support so agents can begin processing results before the full response arrives, which is critical for real-time shopping workflows.

A traditional product feed is a batch export — typically a CSV or XML file generated once or twice per day — that contains a static snapshot of product data. An agent-ready API is a real-time, queryable interface designed for programmatic consumption at machine speed. Key differences include: latency (milliseconds vs. hours), granularity (query specific products vs. download entire catalogs), freshness (live data vs. periodic snapshots), interactivity (agents can filter, sort, and paginate results), and event support (push notifications for changes vs. polling for updates). Agent-ready APIs also enforce strict schemas and versioning so automated consumers never encounter unexpected format changes.

Every data point passes through a multi-layer validation pipeline. First, extraction validation confirms that prices, titles, and attributes were parsed correctly using our AI extraction confidence scoring. Second, temporal validation compares current values against historical baselines to flag anomalies — a $500 TV suddenly priced at $5 is held for verification rather than passed to agents. Third, cross-source validation checks that matched products show consistent attributes across marketplaces. Each response includes per-field confidence scores so agents can implement their own quality thresholds. For autonomous checkout scenarios, we recommend requiring a minimum confidence of 0.95 on price and availability fields.

Entity resolution is the process of determining whether two product listings from different sources refer to the same real-world item. A Sony WH-1000XM5 headphone listed on Amazon with ASIN B0BX2L8PFJ and on Best Buy with SKU 6505727 is the same product, but without entity resolution an agent would treat them as two separate items. Reliable entity resolution is foundational for comparison shopping agents because it enables true apples-to-apples price comparison, prevents duplicate recommendations, and allows agents to route purchases to the retailer offering the best combination of price, availability, and delivery speed for a specific product.

Absolutely. Our APIs are designed to be the data backbone for custom agent development. You get structured product search, real-time pricing, inventory signals, cross-marketplace matching, and historical analytics through clean, well-documented endpoints. We provide quickstart guides for building agents with Python, TypeScript, and Go, along with reference architectures for common patterns like comparison shopping agents, deal-hunting bots, and procurement automation systems. Many customers start with our LangChain or OpenAI function-calling integrations and customize from there. Our solutions engineering team is also available to help design the data flow for complex agent architectures.