Agentic AI for customer service
Help your support team reach the right answer sooner.
Your team spends too long piecing together policies and customer context. Use Canvas to plan a focused support workflow: trusted sources, useful drafts and a clear person responsible for every reply.
Illustrative workflow to plan
A small first version. A clear human decision.
Select a step to follow the handoff. People keep the decisions marked for human review.
Receive request
Identify the request type and missing context.
Plan the evidence and ownership for this handoff.
Example: prepare delivery-status response drafts for a support specialist. Missing order context or policy exceptions go to a person. Canvas helps plan the sources, systems and handoffs; it does not connect to your helpdesk or send replies.
The queue is visible. The missing context is not.
A routine ticket can hide an expired policy, a previous promise or an exception. Choose one request type, identify the knowledge owner and define when a person must take over. A narrower workflow is easier to evaluate.
Research context: an NBER study of AI-assisted customer support found that effects differed across workers. That makes team experience and task mix relevant evaluation questions; its results do not establish an outcome for Canvas or for your support operation.
NBER: Generative AI at WorkBring the evidence behind the idea.
- Describe the common request categories, the queue owner and how a request reaches the right person.
- Bring a sanitized representative scenario and identify the approved policy source, its owner and its update process.
- List situations that require escalation: missing context, conflicting guidance, commitments and complaints.
- Record any earlier pilot, what broke down, and whether the knowledge or review process has actually changed.
Turn discovery into a reviewable plan.
Capture the workflow and its unknowns in a shared Agentic Brain. Generate a blueprint and implementation plan for support and engineering to review. Agree which gaps must be resolved before a pilot.
Define what would make it worthwhile.
Compare handling time, response quality and escalation frequency by request type. Include reviewer time, corrections and knowledge maintenance. Use Agentic ROI to explore assumptions, then agree pilot acceptance criteria with the support owner.
Your first conversation
Start with a brief like this.
We want to explore AI-assisted triage and response drafts for delivery-status requests. A support specialist approves every reply. Our knowledge owner maintains the policy, but we do not yet know how often order context is missing. Help us map the workflow, escalation rules and evidence needed for a small pilot.
Adapt this example and bring it to the home-page conversation. The button opens the home page; it does not fill in the brief.
Start planningBefore you start
Will Canvas run our customer service agent?
Canvas helps define and review the plan. Connecting a helpdesk, implementing the workflow and operating an agent require separate delivery work. Use the blueprint to discuss those responsibilities before committing to a pilot.
Do we need to upload real customer tickets?
Start with descriptions and sanitized representative examples. Explain the shape of the information and where it lives; do not paste sensitive customer details just to illustrate the workflow.
What if our knowledge base is not ready?
Keep that gap visible. You may choose to improve policy ownership, narrow the request category or establish a reliable review process before building an agent. A useful plan can conclude that preparation comes first.
What are agentic AI customer service use cases?
Common starting points are triage of one request type, response drafts a person approves, and escalation when context is missing. The example on this page is delivery-status drafts for a support specialist. It is a planning example, not an installed automation or a customer result.
Is agentic AI customer service the same as a chatbot?
Not necessarily. A chatbot answers customers directly. The workflow planned here prepares drafts and escalations for a person, who approves every reply. Adding a customer-facing bot would be a separate decision.