Fixing AI hallucinations for Meta messaging commerce with structured order flows

Executive Summary

Merchants using Instagram and Messenger get wrong prices, phantom products, and broken order flows from LLM bots. Build structured connectors plus stepwise, domain-locked agents to eliminate hallucinations and reliably capture orders. Meta messaging is widely used for direct sales on Instagram and Messenger, and the source states the builder is already passing conversations to an LLM via Meta Webhooks. Recent LLM improvements enable natural conversation but still fail when external state is not canonicalized, making deterministic agent patterns plus real-time webhook-driven inventory lookups newly implementable and necessary. As more SMB merchants move to conversational commerce, the frequency of order flows increases the cost of repeated hallucination failures, making a reliability-first product timely. By tightly coupling Meta Webhooks, canonical store inventory and pricing APIs, and deterministic stepwise agents that enforce required fields, this product can eliminate the primary failure modes described in the source. The Reddit source explicitly describes a stack that already uses Meta Webhooks and an LLM, and the failure is data and flow mismatch. A connector-first approach creates a practical data moat - cleaned, canonical SKU and order-state streams per merchant - which reduces hallucinations more than a generic LLM wrapper. Frequent conversational commerce interactions on Instagram mean correctness directly affects revenue, creating a clear ROI for merchants.