Building a Context-Aware, Compliant LLM Agent for High-Volume Support Deflection

We engineered a highly restrictive, stateful conversational AI agent utilizing a localized Retrieval-Augmented Generation (RAG) architecture. Instead of relying on a foundational model's pre-trained knowledge, the agent is strictly tethered to CoreTech's proprietary product databases, security boundary documentation, and historical Zendesk transaction paths. The agent operates inside a custom validation framework that monitors semantic inputs in real-time. We utilized deterministic function calling to allow the model to pull live transaction data securely via secure API endpoints. The guardrails are absolute: if a user query falls outside the vectorized documentation or requires account mutation (like transferring funds), the system immediately halts generation and forwards the complex edge case to human personnel, pre-compiling the entire historical memory context so the human agent loses zero time.