Case Study: How We Automated Customer Support Ticket Triage with an AI Agent

Customer support is the backbone of any successful business, but it's often a source of significant operational strain. Teams get bogged down by the manual, repetitive task of triaging incoming support tickets: reading, categorizing, prioritizing, and assigning each one. This process is not only time-consuming but also prone to human error and difficult to scale during peak hours.

What if you could delegate that entire workflow to a reliable, autonomous digital worker?

At bots.do, we don't just talk about theory; we practice what we preach. We built an autonomous AI agent to handle our own customer support ticket triage. This post is a case study of how we turned a critical but repetitive business process into a scalable, on-demand service using our own platform.

The Challenge: The Manual Triage Bottleneck

Before automation, our support workflow faced common challenges:

• Time Lag: Every new ticket sat in a queue waiting for a human to read and classify it, delaying the time-to-first-meaningful-action.
• Inconsistent Classification: Different team members might categorize the same type of issue differently, leading to confusion and tickets bouncing between teams.
• Scalability Issues: A new product launch or a minor service disruption could cause a surge in tickets, overwhelming the team and leading to backlogs.
• High Opportunity Cost: Our skilled support engineers were spending valuable time on administrative tasks instead of solving complex customer problems.

This was a perfect use case for an AI agent—a task with a clear goal, definable rules, and access to digital tools.

The Solution: Building a "TriageBot" with bots.do

We decided to build our own digital worker on the bots.do platform. The concept is simple: you create an autonomous agent by giving it a clear goal and access to the right tools (your APIs). The agent then uses its intelligence to figure out how to achieve that goal.

1. Defining the Agent's Goal

First, we defined a single, clear objective for our agent. This is the core instruction that guides all its actions.

Goal: "Analyze incoming support tickets to determine their category (e.g., Billing, Technical, Sales) and priority (Low, Normal, High, Urgent). Then, tag the ticket appropriately and assign it to the correct support team."

2. Providing the Right Tools

An AI agent is only as powerful as the tools it can use. We gave our TriageBot access to a few key API endpoints that act as its digital hands and eyes:

• tickets.read(ticketId): To ingest the content of a new ticket.
• knowledgeBase.search(query): To look up information about similar past issues or product features to help with classification.
• tickets.update(ticketId, { tags, priority, assignee }): To apply tags, set priority, and assign the ticket.
• users.lookupByTeam(teamName): To find the right team queue (e.g., 'billing-queue', 'engineering-support').

3. Deploying the Agent with a Few Lines of Code

With the goal and tools defined, deploying the agent is incredibly straightforward using our SDK. This "Business-as-Code" approach allows developers to version, manage, and integrate AI agents just like any other piece of software.

import { bots } from '@do/sdk';

// Define the TriageBot agent
const triageBot = await bots.create({
  name: 'SupportTicketTriageAgent',
  goal: 'Analyze incoming support tickets to determine their category and priority. Then, tag the ticket and assign it to the correct support team.',
  tools: [
    'tickets.read',
    'knowledgeBase.search',
    'tickets.update',
    'users.lookupByTeam'
  ]
});

console.log('TriageBot deployed and ready to a.do work:', triageBot.id);

// Now, we can simply trigger this bot via its API endpoint for each new ticket
// bots.run(triageBot.id, { ticketId: 'new-ticket-123' });

The Agentic Workflow in Action

This is where the magic of an agentic workflow shines, separating bots.do agents from simple chatbots or scripted automation. The agent doesn't just follow a rigid, pre-defined path; it reasons and plans.

Here's how the TriageBot handles a new ticket with the subject "Credit card charge error":

1. Ingestion: The agent is triggered and uses the tickets.read() tool to get the ticket's full content.
2. Reasoning & Planning: The agent analyzes the text. It identifies the keywords "credit card" and "charge error." Based on its goal, it forms a multi-step plan:
   • Plan Step 1: The issue is clearly about payment. I'll classify the category as 'Billing'.
   • Plan Step 2: Billing issues are important. I'll set the priority to 'High'.
   • Plan Step 3: I need to find the billing team. I will use the users.lookupByTeam() tool with the parameter teamName: 'billing'.
   • Plan Step 4: Once I have the assignee ID, I will use the tickets.update() tool to apply all changes.
3. Execution: The agent executes its plan, making the necessary API calls in sequence. In seconds, the ticket is correctly categorized, prioritized, and placed in the billing team's queue.
4. Adaptation: What if a ticket is ambiguous? For a ticket like "My integration stopped working," the TriageBot might first use knowledgeBase.search('integration not working') to see if it's a known issue with a specific API, helping it correctly route the ticket to the right engineering sub-team.

The Results: A More Efficient and Scalable Support System

By deploying the TriageBot, we achieved measurable results:

• 95% Automation: Over 95% of incoming tickets are now triaged automatically, without any human intervention.
• 80% Faster Response Time: Tickets are routed to the correct expert in seconds, not minutes or hours. This dramatically reduced our average time-to-first-response.
• Increased Team Capacity: Our support engineers now focus exclusively on solving complex problems, boosting morale and overall productivity.
• 24/7 Scalability: Ticket surges are handled instantly by the bot, ensuring a consistent level of service day or night, weekday or weekend.

Why Build a Digital Workforce?

This case study demonstrates the power of shifting from simple automation to autonomous AI agents. Instead of writing a rigid script for every possible scenario, you empower a digital worker with a goal and let it handle the execution.

The bots.do platform makes this possible by providing the foundation for building, deploying, and managing an entire digital workforce. These agents are more than just code—they are scalable, on-demand services that learn, adapt, and execute complex business workflows.

Ready to automate your repetitive tasks and build your own digital workforce? Explore bots.do and deploy your first agent today.

Frequently Asked Questions

Q: How is this different from a simple chatbot?
A: Unlike traditional chatbots that follow rigid scripts, bots.do agents are built on an agentic workflow platform. They can reason, plan multi-step actions, and use various tools (APIs) to accomplish complex goals, much like a human employee.

Q: What kind of tasks can these bots a.do?
A: Our bots can handle a wide range of tasks, from data entry and report generation to complex customer support, lead qualification, and market research. If a task can be defined as a workflow with digital tools, a bot can be built for it.

Q: Is programming knowledge required to use bots.do?
A: While our SDKs for developers offer the most power and flexibility (Business-as-Code), we are also developing low-code/no-code interfaces to make building and managing bots accessible to everyone.