Customer Support Automation

The field studies how to automate customer conversations and internal technical support, using methods from FAQ matching and retrieval-augmented generation to stateful tool-using agents, diagnostic dialogue, and selective human handoff. Common approaches include fine-tuning language models, reinforcement learning for cost-aware dialogue, prompt and retrieval feedback loops, and safeguards such as policy checks, typed tools, and local processing. Evaluation increasingly tests realistic multi-turn tasks with simulated tools or production backends, measuring resolution quality alongside latency, handling time, customer satisfaction, and policy compliance. A central challenge is balancing reliable, safe responses with speed, cost, and privacy, while ensuring offline benchmarks and automated judges predict performance in live service settings.