Balancing Agency and Reliability in LLM-powered Customer Support Agents
While Large Language Models (LLMs) are increasingly capable of high-agency tasks, deploying them in high-value use cases like customer support requires prioritizing reliability and consistency. Research reveals that while high-agency agents excel in ideal environments, real-world customer support presents challenges: knowledge gaps, unpredictable user behavior, and time constraints. To address this, a novel metric, pass^k, was developed and tested via simulated customer interactions. Results demonstrate that high-agency agents suffer reliability issues with complex tasks. The solution? The "Give Fin a Task" agent, which enhances reliability by restricting agent autonomy and employing step-by-step instructions, decomposing complex tasks into simpler modules. This approach offers a promising pathway for improving LLM performance in real-world customer support.
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