Best Practices and Common Pitfalls of AI

Building AI agents that operate inside transactional systems — booking portals, e-commerce checkouts, invoicing flows — is a different game from a chatbot demo. The moment money moves, hallucinations become chargebacks.

Start with your data, not the model. We train custom models on each client’s business data: product catalogues, booking rules, invoice templates, support history. A generic model asked to “generate an invoice” will invent line items. A scoped model asked to “render invoice #ORD-8842 from the client’s schema” cannot.

Guardrails beat prompt engineering. No prompt reliably stops a model from saying something wrong. We wrap every AI call in a validation layer: schema-validated outputs, allow-listed actions, and hard fallbacks to human review for anything above a confidence threshold. The AI can suggest; it cannot execute a refund without a signed-off rule.

Log everything. Every AI decision in a transactional flow gets a structured log: input, output, model, confidence, rule path. When a customer disputes a charge, you can replay exactly what the AI did and why. This is non-negotiable for compliance.

The pitfall we see most often: teams ship a demo-trained agent straight to production, it works for a week, then drifts. Treat AI like infrastructure — versioned, monitored, and rollback-able. That’s how we keep 24/7 support agents running without incidents on pay day.