How to Build AI Agents That Execute Customer Service Tasks, Not Just Answer Questions
A technical guide published on DEV Community outlines a production-ready architecture for customer service AI agents that go beyond answering questions to actually executing workflows. Unlike standard chatbots that retrieve and respond, these agents classify intent, call backend tools, and complete actions such as processing refunds or triggering return labels. The architecture centers on a multi-step agent loop built around Anthropic's Claude model, with tool-use as the critical layer that connects the AI to live business systems. Intent classification determines which tools and policy rules apply before any action is taken, reducing the risk of incorrect or unauthorized operations. The guide targets developers building real-world deployments, covering areas that most beginner tutorials omit, including escalation logic and conversation state persistence.
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