Example engagementAI agents
A support agent that resolves routine tickets and escalates the rest.
Skilled people stuck on repetitive tickets.
Most support queues are dominated by a handful of routine requests: refunds, address changes, order status. Each one needs lookups in two or three systems. The team spends its day on lookups instead of the tickets that need judgment.
An agent that does the lookups and the action.
The agent reads each ticket, checks billing, orders and your knowledge base, then resolves it or drafts a reply. Anything outside policy, or above a set amount, goes to a person with a summary and the evidence attached.
ArchitectureRuns in your cloud
1 · TriggerNew ticketFrom your helpdesk via webhook
2 · AgentPlan → act → verifyPrimary model with a fallback
3 · ToolsBilling · orders · knowledge baseExposed through an MCP server
4 · ControlGuardrails · approvalSensitive actions need a person
Evals in CITracingCost capsAudit log
Resolution accuracyDid the agent take the right action, checked against labelled tickets.
Escalation qualityDoes it hand off the tickets a person should see, and only those.
Tone & policyReplies follow your voice and your refund and privacy policies.
Cost & latencyCost per ticket and time to resolution, tracked per release.
- Week 1–2Agent Sprint: map ticket types, label a sample, build a prototype and an eval baseline.
- Week 3–6Build tools and integrations, add guardrails, grow the eval suite from real tickets.
- Week 7–9Shadow mode: the agent drafts, people approve. Compare against the evals.
- Week 10+Staged rollout by ticket type, monitoring, and handover to your team.
- Agent code in your repository
- MCP server for your tools
- Eval suite and CI gates
- Dashboards and alerts
- Runbooks and a handover session
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