AI customer service challenges and solutions: A playbook | Decagon

This post contributes to the AI Support Agent Platform by detailing strategies to prevent chatbot failures, specifically hallucinations and knowledge base decay. It introduces architectural solutions like Retrieval-Augmented Generation (RAG) and action-constrained AI agents as primary defenses against incorrect information. It also highlights the importance of data privacy, compliance, and best practices for customer experience, including setting clear expectations, providing escape hatches, and using A/B testing. The post outlines a structured, collaborative setup process with Decagon and emphasizes a controlled failure approach with confidence thresholds and nightly evaluations as key to preventing failures.