Customer service is how companies interface with their customers. It can
contribute heavily towards the overall customer satisfaction. However,
high-quality service can become expensive, creating an incentive to make it as
cost efficient as possible and prompting most companies to utilize AI-powered
assistants, or "chat bots". On the other hand, human-to-human interaction is
still desired by customers, especially when it comes to complex scenarios such
as disputes and sensitive topics like bill payment.
This raises the bar for customer service agents. They need to accurately
understand the customer's question or concern, identify a solution that is
acceptable yet feasible (and within the company's policy), all while handling
multiple conversations at once.
In this work, we introduce "Ask Me Anything" (AMA) as an add-on feature to an
agent-facing customer service interface. AMA allows agents to ask questions to
a large language model (LLM) on demand, as they are handling customer
conversations -- the LLM provides accurate responses in real-time, reducing the
amount of context switching the agent needs. In our internal experiments, we
find that agents using AMA versus a traditional search experience spend
approximately 10% fewer seconds per conversation containing a search,
translating to millions of dollars of savings annually. Agents that used the
AMA feature provided positive feedback nearly 80% of the time, demonstrating
its usefulness as an AI-assisted feature for customer care.

利用大型语言模型作为服务界面的附加功能，Ask Me Anything (AMA) 允许客服代理人在处理客户对话时随时向模型提问，模型实时提供准确的回答，节省了代理人的时间和成本。内部实验结果显示，使用 AMA 功能的代理人与传统搜索经验相比，每个包含搜索的对话节省了约 10% 的时间，年度节约金额达到数百万美元。使用 AMA 功能的代理人在接近 80% 的时间内提供了积极的反馈，证明其作为人工智能辅助功能在客户服务中的可用性。

问我任何问题”：康卡斯特如何使用 LLMs 实时辅助代理人

"Ask Me Anything": How Comcast Uses LLMs to Assist Agents in Real Time

The landscape of software development has witnessed a paradigm shift with the
advent of AI-powered assistants, exemplified by GitHub Copilot. However,
existing solutions are not leveraging all the potential capabilities available
in an IDE such as building, testing, executing code, git operations, etc.
Therefore, they are constrained by their limited capabilities, primarily
focusing on suggesting code snippets and file manipulation within a chat-based
interface. To fill this gap, we present AutoDev, a fully automated AI-driven
software development framework, designed for autonomous planning and execution
of intricate software engineering tasks. AutoDev enables users to define
complex software engineering objectives, which are assigned to AutoDev's
autonomous AI Agents to achieve. These AI agents can perform diverse operations
on a codebase, including file editing, retrieval, build processes, execution,
testing, and git operations. They also have access to files, compiler output,
build and testing logs, static analysis tools, and more. This enables the AI
Agents to execute tasks in a fully automated manner with a comprehensive
understanding of the contextual information required. Furthermore, AutoDev
establishes a secure development environment by confining all operations within
Docker containers. This framework incorporates guardrails to ensure user
privacy and file security, allowing users to define specific permitted or
restricted commands and operations within AutoDev. In our evaluation, we tested
AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and
87.8% of Pass@1 for code generation and test generation respectively,
demonstrating its effectiveness in automating software engineering tasks while
maintaining a secure and user-controlled development environment.

AutoDev 是一个全自动化的 AI 驱动的软件开发框架，旨在实现复杂软件工程任务的自主规划和执行，通过自动化的方式执行任务并确保安全和用户控制的开发环境。