A manufacturing business fields the same customer question many times a day: where is my order, and when will it arrive? Answering it well takes a person who opens the ERP, reads the order record, and writes a clear reply. Answering it at volume takes time away from work that grows the business.
Mirai360 builds AI agents that take over this one narrow task: reading the order-status question, checking the order system, drafting a reply, and escalating anything unusual to a person. This post describes how the agent works and the controls that keep it accountable.
What problem does this agent solve?
Customer service and sales teams in manufacturing firms spend a meaningful share of their day on order-status and delivery questions. Each question is simple on its own: check the ERP, confirm the stage, tell the customer. Repeated across dozens or hundreds of orders a week, the task consumes hours that could go to new orders, quality issues, or account management. The work is also repetitive enough that it invites errors: a missed email, a stale answer, an inconsistent tone between two replies about the same order.
What does the agent actually do?
The order-status agent performs three steps for each incoming question, in order:
- Read the order system. The agent looks up the order in the ERP or order-management system the business already uses. It checks the current stage, expected delivery date, and any flags on the order.
- Draft a reply. The agent writes a response in plain language, addressed to the specific question asked, using the order data it just read. The reply matches the tone the business already uses with its customers.
- Escalate exceptions. If the order is delayed, flagged, disputed, or otherwise outside the normal pattern, the agent does not send a reply on its own. It routes the case to a person, with the order data attached, so the person answers with full context instead of starting from zero.
If the order system has no answer, the agent does not guess one. It also will not promise a delivery date the ERP has not confirmed.
How is this different from a standard chatbot?
A scripted chatbot answers from a fixed set of responses and cannot read a specific order record; it either matches a canned answer to a generic question or hands off immediately. The order-status agent reads the actual order in the ERP for the specific customer asking, and drafts a reply built from that record. The difference matters most on volume: a chatbot handles the easy cases and passes everything else along; this agent handles the correctly scoped cases and escalates the rest with the order data already attached, so the human does not start from zero.
Where does a human stay in control?
A human reviews or handles every exception the agent flags. The boundary is set on purpose: order-status questions vary in stakes. A routine confirmation carries little risk if the agent answers it. A delayed shipment or an unhappy customer carries reputational and commercial risk that belongs with a person. The agent clears the routine volume and routes the rest to the right person, so a person's judgment stays on the cases that need it.
Every action the agent takes, and every case it escalates, is logged from the first day of deployment. The business can review what the agent answered, what it escalated, and why, at any time.
Why start with a measured baseline?
Before the agent goes live, Mirai360 works with the business to record a baseline: how many order-status queries the team currently handles, how long a reply currently takes, and how the team currently judges whether a reply was good. The exact metric definitions and measurement method are set with each client. The baseline gives the business a fixed point to compare against once the agent is live, instead of a general impression that things feel faster.
The agent is scoped to this one workflow at launch. It does not expand into other customer-service tasks, pricing questions, or complaint handling until the business reviews the baseline result and agrees, in writing, that the agent should take on more.
How does Mirai360 keep the agent inside its boundaries?
Three controls apply to this agent. These controls match Mirai360's approach to every deployment:
- A measured baseline before deployment. The business and Mirai360 agree on what “faster” and “correct” mean for this workflow before the agent answers a single live query.
- A human decision on every exception. The agent escalates; it does not guess. A named person on the client's team owns the exception queue.
- An audit trail for every action. Every reply the agent sends and every case it escalates is recorded, so the business can review the agent's work at any time.
Scaling the agent to a second workflow, a higher volume, or a broader set of order types happens only against numbers the business and Mirai360 agree on in advance, not on the strength of a good first few weeks.
What changes for the customer?
A customer who asks about an order gets a reply built from the same order data a staff member would check, without waiting for a person to become available. A customer whose order needs a human answer, because it is delayed, flagged, or unusual, still reaches a person, and that person already has the order details in front of them instead of starting the lookup from scratch.
Where does Mirai360 fit?
Mirai360 AI provides the platform to build, run, and govern AI agents like this one: an LLM gateway to connect the agent to the ERP and other systems, guardrails to keep it inside the order-status task, and an audit trail so every action is reviewable. The order-status agent is one narrow workflow built on that same platform, delivered self-hosted, managed, or custom-built depending on what the business already runs.
Talk to us
A 30-minute call is the place to look at a specific order-status or delivery-support workflow that already creates repetitive load on a team, and to scope what a measured, human-controlled agent would look like for it: https://calendly.com/shivang-mirai360/30min
FAQ
- What problem does this agent solve?
- It clears repetitive order-status and delivery questions that otherwise take hours a week away from a manufacturing team's other work.
- What does the agent actually do?
- It reads the order in the ERP, drafts a reply from that record, and escalates delayed, flagged, or disputed orders to a person.
- How is this different from a standard chatbot?
- A chatbot answers from fixed scripts and cannot read a specific order record. This agent reads the actual order and drafts a reply built from it.
- Where does a human stay in control?
- A human handles every exception the agent flags, and every action the agent takes is logged in an audit trail.