Real-time price and availability feeds are the lifeblood of agentic commerce, enabling AI buyers to make accurate, timely procurement decisions. Unlike traditional batch APIs, these feeds must push updates instantly using patterns like Change Data Capture (CDC) and WebSocket connections to reflect dynamic market conditions. This architecture ensures AI agents operate on a consistent, current view of inventory and pricing, which is critical for building trust in autonomous systems. The core challenge is balancing high-frequency updates with system stability and data integrity across distributed services.
Guide
How to Implement Real-Time Price and Availability Feeds for AI
A technical walkthrough for building low-latency, high-frequency data feeds to power autonomous AI buyers. Implement CDC, in-memory caching, and push APIs.
This guide provides the technical foundation for building the low-latency data infrastructure that powers autonomous AI buyers.
You will implement a feed that handles both bulk queries from agents performing initial research and streaming updates for monitoring specific items. This involves an in-memory database like Redis for sub-millisecond reads, coupled with a durable event log like Apache Kafka for reliable message propagation. By the end, you'll have a production-ready system that supports the high-concurrency, low-latency demands outlined in our guide on Setting Up an Agent-Readable Inventory Feed, forming a key component of a robust AI Buyer Optimization strategy.
DATA SYNC ARCHITECTURE
Technology Comparison: CDC vs. Polling vs. Event Sourcing
A comparison of three core patterns for building real-time price and availability feeds, evaluated on criteria critical for AI buyer agents.
| Architectural Feature | Change Data Capture (CDC) | Database Polling | Event Sourcing |
|---|---|---|---|
Data Freshness (Latency) | < 1 second | 30 seconds - 5 minutes | < 100 milliseconds |
Database Load Impact | Low (log-based) | High (query-based) | None (source of truth) |
Historical Data & Audit Trail | |||
Implementation Complexity | Medium | Low | High |
Handles Bulk Agent Queries | |||
Infrastructure Cost | $$ | $ | $$$ |
Best For | Low-latency updates from existing DB | Simple, low-frequency checks | Complex, audit-critical business logic |
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Common Mistakes
Building real-time feeds for AI agents is a high-stakes engineering challenge. These are the most frequent technical pitfalls that cause latency, data inconsistency, and agent errors.
Stale data occurs when you use pull-based polling instead of push-based architecture. AI agents making bulk queries will overwhelm your API with requests, creating a lag between a data change and its availability.
The fix is to implement a publish-subscribe pattern:
- Use Change Data Capture (CDC) tools like Debezium to stream database changes.
- Push updates via WebSockets or Server-Sent Events (SSE) to connected agents.
- Cache the latest state in an in-memory database like Redis for instant query responses.
Without this, agents base decisions on outdated prices or stock levels, leading to failed transactions and lost trust.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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