A Practical Approach to Building RLDriven Support Agents

Reinforcement learning introduces a new structure for automated decisionmaking in support systems. The paper demonstrates how an agent learns escalation behavior from outcomes rather than preset rules. The model evaluates each request using ticket history, customer context, and available knowledge base content. Feedback from a judge model guides the policy, producing steady improvement across iterations.

Organizations can adopt this approach by defining workflows that allow the agent to explore actions, receive structured reward signals, and align with established support policies. The whitepaper describes how DSPy produces realistic training scenarios and how the Weights and Biases stack coordinates GRPO training, model hosting, and evaluation.