AI agents for customer support

01

Choose the support job before the channel

Repeated questions, status enquiries and incomplete requests are possible starting points. First determine whether better documentation, search, a form or fixed routing would solve the problem. The agent versus chatbot comparison explains when adaptive tool use adds something useful.

Look for a category with maintained answers, a known human owner and a manageable failure consequence. Do not start by delegating every complaint, billing dispute and account-access issue to the same unrestricted assistant.

• Define who is being supported and what service they use.
• List the exact requests included in the first release.
• Separate general guidance from information about a particular account.
• Identify actions the system must never take.
• Give customers a clear route to a person.

LCR’s general AI-agent offer includes support and request triage. The specific systems, actions and support responsibilities still need to be agreed for your project.

02

Make the next step verifiable

A support journey should distinguish an explanation from an action. “Here is how to update your details” is not the same as “your details have been updated”. The latter needs confirmation from the owning system and appropriate authority.

Make unavailable services visible. A timeout should produce a recoverable status and a fallback, not a fabricated answer. For connected steps, use the AI integration contract to define retries, duplicate handling and confirmed outcomes.

03

Separate knowledge from access to customer records

Give approved support content an owner and a review process. Mark outdated or withdrawn information so it is not used as current policy. Test contradictory sources and questions with no supported answer; the assistant should be able to say it cannot establish the answer.

For personal or account-specific information, the application must establish identity and authorise the particular record before providing it to the agent. Knowing a name, email address or order number is not automatically sufficient proof of access.

Keep permissions in trusted application controls. Retrieved documents and customer messages may contain instructions, but they should not change the agent’s authority. The business-system access guide covers the underlying controls.

Review what information goes to suppliers and appears in logs. Collect only what the support task needs, and agree retention and review access with the responsible privacy and security owners. This workflow design is not itself a compliance assessment.

04

Make escalation part of the service

Handover should be available when a customer requests a person, sources conflict, the task is outside scope, an action fails or the consequences require human judgement. Do not make the customer repeat the same explanation just to cross that boundary.

Provide a concise summary of the request, relevant source or record references, steps already tried and the unresolved question. Label uncertain interpretations. Avoid passing unnecessary sensitive material into notifications.

The receiving team needs a queue and ownership rules. If it is unavailable, explain how the request is recorded and what the customer can do next. Only show a response-time commitment the business has actually approved. See the human handover guide for the operational detail.

Keep complaints, compensation decisions, account recovery and other consequential matters human-led. A confidence score or reassuring tone must not replace the required authority.

05

Start with an answer-and-triage release

Hypothetical example, not an LCR case study: a service team starts with one recurring setup question. The assistant explains approved steps, asks which step failed and prepares a handover if the issue persists. It cannot change account permissions or make service commitments.

Test ordinary requests, unfamiliar wording, conflicting documents, unavailable systems and repeated attempts to obtain another customer’s details. Check both the response and the resulting ticket or handover state. Anthropic’s evaluation guidance discusses why agent outcomes need to be evaluated alongside their interactions.

Keep a named operator able to pause the workflow and recover unresolved work. Expand the first release only after the team can see what is working, what still fails and who handles it.

06

Measure resolution, recontact and effort together

Measure correct resolution for the chosen category, repeat contacts, reopened cases, correction effort and escalation acceptance. Review customer feedback and sample apparently successful conversations to find hidden failures.

Avoid a target that rewards keeping requests away from people regardless of quality. Compare similar request categories and demand levels before and after the change. Keep serious safety and privacy failures visible rather than blending them into an average score.

Use the AI agent ROI guide to account for staff review, retries and ongoing content maintenance. Scope the first useful outcome with the team rather than promising a universal saving or timeline.

Bring your frequent requests, approved answers, existing ticket flow, access rules and escalation owner to the initial discussion. If platform choice is still open, compare the custom, platform and hybrid options against that same scope.

Questions

Frequently asked questions