The Buyer's Guide to AI Agents for Customer Service

AI customer-service agents vary widely despite being marketed under the same label, ranging from predictable rule-based bots to governed hybrids that constrain LLM reasoning through explicit workflows and fully autonomous systems that rely more heavily on model judgment. Buyers are advised to evaluate vendors first by who controls the agent’s logic, then by whether detailed execution traces reveal why specific decisions occurred, and finally by whether pricing incentives align with customer value. Governed systems generally provide greater predictability, configurable guardrails, model flexibility, and easier debugging, while autonomous systems may appear more capable in demonstrations but can become difficult to diagnose at scale. Resolution-rate metrics alone may obscure important failures, so vendors should be able to trace failed conversations to specific knowledge lookups, tool calls, or API interactions. The text also argues that resolution-based pricing can incentivize vendors to define success loosely or limit capabilities, whereas usage-based pricing may better support transparency and scaling. Rather than relying on curated demos, organizations should run pilots that test difficult conversations, model portability, pricing at higher volumes, and handoffs across channels such as voice and chat.

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