Every one of them runs on the systems you already have, inside the permissions your team already holds, with the approval checkpoints wherever you want them. Three of them are in production now.
The four patterns
- Front desk
- Inbound calls and messages: booking, rescheduling, cancellations, and billing questions. Answers on the second ring at any hour, works the phone line and calendar you already run, and hands over anything outside its policy with the account context already gathered.
- Revenue operations
- Lead intake through to a booked call. Scoring against your ICP, enrichment before the CRM write, follow-up on every lead within minutes, and early flags on churn signals. Runs inside the CRM your team already works in.
- Support operations
- Triage, first-line replies, guided account changes, and escalation packaging. Built on your own help docs and resolved tickets, running inside your existing helpdesk. Written up in full: AI agents for customer support.
- Back office and finance
- Invoice capture and approval routing, statement matching, duplicate detection, and the evidence trail for close. The pattern where a wrong action is most expensive, so approval checkpoints are heaviest here. The reconciliation deployment shows where that boundary sits.
What they have in common
- They run on your stack
- CRM, ERP, helpdesk, accounting, phone, email. Agents reach them through the same APIs your team uses. Nothing migrates and nothing gets replaced.
- They stop where you tell them to
- The boundary between what an agent does alone and what needs a person is set before launch and reviewed monthly. It is a judgment about your customers and your risk, so an operations lead owns it.
- Everything is logged
- Every action, with what the agent saw and what it decided. That record is what makes a wrong action reviewable.
- They start by drafting
- No agent goes straight to autonomous. Each starts in shadow mode on your live work, then supervised, then autonomous per workflow once it clears a bar your team set.
Going deeper
Each pattern has its own failure modes, its own rollout order and its own metrics. The detailed write-ups cover what an agent of that kind takes over first, where deployments of that kind break, and what to measure so you can tell a working one from a flattering dashboard. Every guide is indexed here, and anything missing goes to team@agentintegrator.io.
Keep reading
Agents by function
AI agents for customer support
What a support agent can take over, where it breaks, and how to roll one out without damaging the queue. From the team that builds and runs them.
Case studies
Three deployments, running now
What three live agent systems do, how each was rolled out, and what changed for the team that used to do the work by hand.
Last updated August 12, 2026. Questions this page did not answer go to team@agentintegrator.io, or start with the audit.
