Build It Right: An AI Agent Masterclass for Customer Support

The biggest takeaway from Botco.ai’s Build It Right webinar is simple: an AI agent should do more than answer customer questions. It should help resolve the problem that brought the customer there in the first place.

In this masterclass, Kevin Clark, Director of Customer Success at Botco.ai, and Piyush Parikh, CEO and Co-founder of Equinox Agents, explored what it takes to build AI agents that deliver measurable business value.

Drawing on their experience in enterprise AI and customer support operations, they discussed how to select the right workflows, give AI agents the context and integrations they need, design effective human handoffs, and measure results beyond chatbot engagement.

Their shared message: Start with the customer journey, not the technology.

Here are the practical lessons support leaders can use to build AI agents that resolve issues, reduce friction, and make human teams more effective.

An AI Agent Masterclass for Customer Support

Lesson 1: Design the customer journey before you build the AI agent

One of the biggest mistakes organizations make is starting with the technology rather than the customer problem.

Piyush explained that companies often deploy an AI agent expecting it to solve problems automatically, without sufficiently considering the information, oversight, and improvements it will require.

“But it’s never a set-it-and-forget-it thing. It’s always a continuous improvement piece.” — Piyush Parikh, CEO and Co-founder, Equinox Agents

Kevin reinforced the importance of designing the customer experience first.

“You don’t want to start with the technology. You want to think about the problem.” — Kevin Clark, Director of Customer Success, Botco.ai

Before choosing an AI platform, support leaders should answer three questions:

1. What does the customer want to accomplish? Identify the actual outcome, not simply the question being asked.
2. Does the AI agent have enough context? Determine whether it can access the relevant policies, customer information, and approved knowledge.
3. Can it take the necessary action? Establish whether it can complete the workflow or transfer the request to someone who can.

For example, a customer asking about an appointment may simply need information. Another customer may need to reschedule, which requires access to a calendar and the ability to update a booking.

These are different workflows, with different integration and escalation requirements.

Masterclass takeaway: Map the journey, define the outcome, and only then decide where AI belongs.

 

Lesson 2: Not every support problem should be automated

Piyush introduced a practical framework drawn from Equinox Agents’ experience managing customer support operations.

His recommendation was to examine existing support tickets, categorize them, and identify the most effective way to handle each type of request.

The four-part support optimization framework

1. Eliminate

Fix the underlying issue so customers no longer need to contact support.

2. Deflect

Use AI or self-service to resolve suitable requests without a human agent.

3. Optimize

Equip human support teams with AI assistance to work more effectively.

4. Rebalance

Route work to the appropriate people based on complexity and expertise.

The first category is especially important. If customers repeatedly contact support because of a confusing product feature or broken process, fixing that problem may create more value than automating the resulting tickets.

For AI deployment, Piyush recommended prioritizing high-volume, lower-risk requests that offer meaningful operational benefits.

“I like to pick the high-volume ones first.” — Piyush Parikh

Kevin added a caution about scaling too quickly:

“The risk doesn’t come in the process of building, comes in the process of scaling.” — Kevin Clark

Rather than attempting to automate every support scenario, organizations can begin with one or two clearly defined ticket categories, establish a baseline, test performance, and expand based on results.

Masterclass takeaway: The goal isn’t to automate the most work. It’s to improve the right work.

 

Lesson 3: Give AI agents the knowledge, integrations, and authority to resolve issues

A capable language model alone is not enough to deliver enterprise customer support.

An AI agent needs trusted information about the business, access to relevant systems, permission to perform defined actions, and clear boundaries for when a human must intervene.

Kevin described a maturity ladder for enterprise AI agents.

The AI agent maturity ladder

01

Answers questions

Responds to customer prompts

02

Knows your business

Uses approved company knowledge and context

03

Takes action

Connects to APIs and operational systems

04

Completes workflows

Handles multi-step requests from beginning to end

05

Improves outcomes

Measures resolution, experience, and business value

The transition from answering questions to completing workflows is what makes AI agents operationally valuable.

“The leap from chatbot to AI agents happens when AI moves from answering to acting.” — Kevin Clark

Consider a customer who says, “I need to reschedule my appointment.”

A basic chatbot might direct the customer to a scheduling page.

A workflow-enabled AI agent could, when properly integrated and authorized, authenticate the customer, check availability, change the appointment, update the CRM, and send a confirmation.

The customer doesn’t need another set of instructions. They need the task completed.

For enterprise deployments, these capabilities also require appropriate security, authentication, business rules, and access controls. In regulated industries such as healthcare, safeguards for sensitive information and human escalation are essential.

Importantly, Piyush warned against integrating every available system without a clear business case. He described a customer project in which months of integration work were being directed toward a ticket category representing just 2% of requests.

The lesson: Let workflow priorities determine your integrations—not the other way around.

 

Lesson 4: Design the human handoff as carefully as the AI experience

Even the most capable AI agent will encounter requests it should not resolve independently.

The question is what happens next.

Piyush emphasized that customers shouldn’t have to repeat their information after being transferred from an AI agent to a human representative.

“The agent should have the full history.” — Piyush Parikh

An effective escalation process should preserve the conversation, relevant customer information, actions already attempted, and reason for escalation.

It should also support follow-up when customers express dissatisfaction.

Equinox Agents approaches this by defining responsibilities for both human and AI agents, including how AI can assist human representatives during more complex conversations.

This shifts the discussion from replacing people to designing a support operation where AI and humans work together.

For support leaders, that means defining what AI should resolve independently, where it should act as a copilot, and which interactions require human judgment or empathy.

Masterclass takeaway: A successful handoff is part of resolution, not a failure of automation.

 

Lesson 5: Think omnichannel, not just voice versus chat

Organizations frequently approach AI deployment by asking which channel to launch first: voice, web chat, or SMS.

Kevin challenged that framing.

“The right question really is, how should customers move between these channels?” — Kevin Clark

A customer might begin with a voice conversation, receive confirmation through SMS, and later speak to a human representative.

Those interactions should be connected rather than treated as separate support journeys.

Kevin described the opportunity to use different channels for different strengths: voice for conversational interactions, chat for information and clarity, SMS for confirmations, and human representatives for escalations.

During the webinar, he also demonstrated a voice-based scheduling interaction that gathered information and confirmed an appointment.

The important feature wasn’t simply that the agent could speak. It was that the conversation supported a defined customer-support workflow.

Masterclass takeaway: The best channel strategy minimizes customer effort and preserves context across every interaction.

 

Lesson 6: Measure AI agent ROI by outcomes, not activity

AI adoption creates a new measurement challenge.

A chatbot can generate thousands of conversations, but that doesn’t necessarily mean it is solving customer problems.

Piyush explained that his customers ultimately want to understand the return they’re getting from their investment.

“What value are you providing? And I’m paying you for this, what’s my return?” — Piyush Parikh

He recommended evaluating whether organizations are eliminating unnecessary requests, deflecting suitable tickets, maintaining customer satisfaction, and reducing support costs.

A useful measurement framework looks beyond activity metrics.

Activity metrics	Business outcome metrics
Number of conversations	Successful resolution rate
Number of messages	First-contact resolution
Bot usage	Customer satisfaction (CSAT)
Ticket deflection alone	Cost per resolution
Volume of automated interactions	Customer effort and escalation quality

The key is establishing what success should look like before deployment.

Piyush described setting expectations for what a project should achieve in its first 30 days and then comparing those expectations with actual performance.

Kevin connected this to investment decisions: once teams understand which workflows deliver measurable value, they can determine where further AI investment makes sense.

Masterclass takeaway: A successful AI deployment should make customer support demonstrably better, not merely more automated.

 

Your Build It Right checklist: Six actions to take next

Kevin and Piyush’s discussion offers a practical starting point for support teams.

AI agent implementation checklist

0 of 6

Identify one high-friction customer journey with a clear outcome.

Review support tickets and select a repeatable, lower-risk workflow.

Determine the knowledge, integrations, and permissions the AI needs.

Define AI responsibilities, human handoffs, and escalation rules.

Establish baseline resolution, satisfaction, and cost metrics.

Launch a focused pilot, measure performance, and refine before expanding.

The common thread across all six actions is that AI agent implementation is an ongoing operational discipline, not a one-time technology installation.

Organizations that begin with a well-defined problem, invest in the right context and integrations, and continuously evaluate outcomes are better positioned to scale AI responsibly.

 

Frequently asked questions about AI agents for customer support

What is an AI agent for customer support?

An AI agent for customer support is a system that uses AI to understand customer requests, access relevant business information, and, when connected to appropriate systems, take actions that help resolve those requests. Unlike a basic question-answering chatbot, a workflow-enabled agent can perform tasks such as scheduling, updating records, or initiating approved support processes.

What is the difference between a chatbot and an AI agent?

A chatbot primarily responds to questions or directs customers to information. An AI agent can go further by using business context, integrations, and authorized actions to complete customer-support workflows. The distinction is not simply how conversational the technology sounds, but what it can accomplish.

Where should a company start when building an AI agent?

Start with a high-volume, repeatable, relatively low-risk support workflow. Review existing tickets, define the desired customer outcome, identify the necessary information and integrations, and establish success metrics before launching.

How should AI agents and human support teams work together?

AI agents can handle suitable repeatable requests, assist representatives with information, and transfer complex or sensitive issues to humans. Effective handoffs should preserve conversation context so customers don’t have to repeat themselves.

How do you measure the ROI of AI agents?

Measure successful resolutions, first-contact resolution, customer satisfaction, customer effort, escalation quality, and cost per resolution. Compare performance against a baseline and evaluate whether the deployment improves the overall support experience.

Final takeaway: Build AI agents around resolution

The most important lesson from Build It Right isn’t about choosing a particular model, channel, or integration.

It’s about being intentional.

Start with the customer problem. Build the workflow around the outcome. Give the AI agent the context and tools it needs. Keep humans involved where they add the most value. Then measure whether the experience actually improved.

That’s how organizations move beyond deploying AI for its own sake and begin building customer-support systems that create lasting business value.

Ready to build AI agents that do more than answer questions? Explore how Botco.ai helps organizations connect AI-powered conversations with business workflows, enterprise integrations, and customer-support outcomes.