Current approaches to customer-facing AI agents all have serious limitations: Fine-tuning is expensive and inflexible, requiring complete retraining for simple changes while risking data leaks. RAG systems help with knowledge but can't guide behavior. Graph-based frameworks become overwhelmingly complex as agents grow more sophisticated.

The core challenge isn't just a technical ability to reach accurate results—it's about being able to shape and update agent behavior easily and dynamically, just like you'd guide human customer service representatives.

See full blog post: https://www.parlant.io/blog/rethinking-how-we-build-customer-facing-ai-agents/