Choose a bounded support task
Start with one task and its current workflow: finding an approved policy, checking delivery status or drafting a reply for a support agent. Define the evidence that proves the task is resolved. A generated answer, a closed ticket and a customer’s completed request are different events.
Record excluded tasks and the conditions for handing the conversation to a person. Compare the pilot with the current support process on the same cases. Do not begin with a universal automation target; the achievable scope depends on the questions, knowledge quality, integrations and review requirements.
Connect answers to approved knowledge
Identify who owns each policy and how changes reach the assistant. Preserve the document version and relevant source behind a response. Test expired offers, conflicting policies, missing information and questions outside the supported scope. Provide a useful escalation when the knowledge base cannot support an answer.
Keep customer-specific records separate from general policy retrieval. Enforce account permissions when reading an order or subscription, rather than trusting a model-proposed identifier. Validate tool inputs and confirm completed updates with the receiving system. The tool-contract guide explains this execution boundary.
Make handoff an explicit workflow
Define the triggers for escalation: a missing source, unresolved tool result, repeated misunderstanding, requested human assistance or a task outside the permitted scope. Pass a concise conversation summary, relevant record identifiers and attempted actions to the receiving agent. Include unresolved status so the agent does not mistake a proposed change for a completed one.
Test queue assignment, duplicate ticket creation and the behavior when the receiving team is unavailable. Tell the customer what happens next using the actual service policy. Do not promise immediate human assistance unless staffing and queue operations support that promise.
Evaluate routing and sentiment signals
Define ticket categories with the support team and include ambiguous or multi-topic examples. Evaluate routing by category and review misrouted cases. Keep customer urgency and business impact grounded in the ticket evidence rather than a label alone.
For a sentiment feature, evaluate the labels on representative language, tone and context. A negative-sentiment score does not establish a customer’s emotional state or the importance of the request. Use it as a reviewable signal, with a correction route, rather than an automatic basis for judging an agent or denying service.
Measure verified resolution and workload
Track supported task completion, unresolved conversations, repeat contacts, escalations, incorrect answers and review corrections. Measure customer satisfaction using the stated survey method and response population. Report the sample size and observation period so a reviewer can assess what the result covers.
Separate first response time from resolution time. Count the review and recovery work that automation creates. Estimate cost per resolved task using model usage, integration, hosting, human review and support costs. An apparent reduction in ticket volume may reflect abandoned conversations rather than successful resolution.
Release with a review and maintenance plan
Begin with a bounded task set and audience, then expand after reviewing the measured outcomes. Maintain a fallback to the existing support process. Version the knowledge collection, model configuration, prompts and tools, and rerun relevant evaluation after changes.
Agree access, retention and deletion requirements for conversations and account data. Assign owners for knowledge updates, routing disputes, failed actions and incident response. A handover should include task definitions, permission and data-flow records, the evaluation report and an operating runbook.
For wider architecture choices, read the agent development guide. Explore AI support-workflow planning or discuss a scoped customer-service pilot with TensorBlue. Project prices and expected benefits should follow the actual workload and pilot evidence.