arXiv:2606.08867v2 Announce Type: replace Abstract: The rapid rise in LLM capabilities has made AI agents increasingly viable across a broad range of tasks. Among the most promising applications is building production-ready customer-facing agents, a challenge that demands coordinated excellence in evaluation methodology, context engineering, training, and online measurement. Yet these critical pillars are typically developed in isolation, creating blind spots that only surface after deployment. In this paper, we present a unified framework that bridges offline development with online impact fo
The rapid advancements in LLM capabilities are making AI agents increasingly viable for complex tasks, pushing the need for robust deployment frameworks.
This paper outlines a unified framework for deploying production-ready customer support AI agents at massive scale, addressing critical evaluation and measurement challenges.
The approach bridges offline development with online impact, potentially accelerating the reliable integration of AI agents into large-scale customer operations.
- · AI agent developers
- · Customer service industries
- · Software-as-a-Service providers
- · Large enterprises
- · Companies with poor evaluation methodologies
- · Traditional call center operations
More sophisticated and reliable AI agents will be deployed in customer-facing roles.
Reduced operational costs and improved customer satisfaction for companies adopting these frameworks.
Further acceleration in the displacement of human agents in routine customer support, shifting human roles to more complex problem-solving and oversight.
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Read at arXiv cs.CL