How to use AI agents for conversational support automation

AI agents are changing B2B customer support in most industries. Instead of team members covering every conversation with customers, these automated tools can answer simple questions and direct more complex conversations to the right people.

If you run a B2B support team and are evaluating how to use AI agents on your team, it’s good to know what these tools can realistically handle. In this AI agent tutorial, we’ll cover how to set them up to support your customer experience and how to measure whether they’re actually working.

Common use cases for AI support agents

Not every support interaction needs to be handled by your support team, like the following examples:

• Repetitive questions. If your team regularly answers the same simple questions from customers (e.g., how do I invite a user to my workspace, where can I find your API reference), they can train an AI agent to answer them independently. Your support team gets time back for complex, high-value conversations.
• Personalized responses at scale. When an AI agent has access to account data, it can tailor answers to the customer context. For example, a customer on your enterprise tier and someone on your starter plan asking about API limits will get different answers without having to mention which plan they’re on.
• Triage and routing. An AI agent can classify an issue, assess urgency, and route it to the right team. That means a billing question goes to your accounts team, and a bug report will route to technical support with the relevant details attached.
• Simple but multi-step processes. If a customer wants to cancel their subscription, an AI agent can handle manual parts of the process without taking time from your support team. Pre-programmed runbooks outline which steps the AI agent should follow in common situations. You can have the agent ask questions and gather feedback for your pre and post-sales teams.
• Support team hand-offs. The best AI agents know when to hand a conversation over to a team member. When a conversation gets complex or a customer is frustrated, the agent can route the conversation to one of your support members with full context — what the customer asked, what the AI agent already tried, and any relevant account details. Your team picks up mid-conversation, and the customer doesn’t need to recount the whole story again.

Setting up an AI agent for customer conversations: 5 steps

Here’s the process for deploying an AI agent that works for most B2B support teams, even without a developer. 

1. Define the AI agent’s goals and support use cases

When you’re figuring out how to make an AI agent work for your team, start by considering what you want it to do. “Handle all support” probably isn’t realistic. But “resolve tier-one questions about integrations and account setup” is.

Be specific about which conversation types the agent will own, which it will assist with, and which it won’t touch at all. Topics that regularly have a low number of ticket touches and fast time to resolution are signs it’s something you can automate, but complex tickets on issues that are often escalated or have a longer time to resolution are generally better handled by your team.

2. Map conversation flows and escalation paths

For every use case you’ve defined, map out the whole conversation, from the customer’s question to when the agent should escalate. The biggest deployment failures happen when teams skip this step and let the agent navigate conversations on its own, which often leads to frustrated customers and higher churn risk.

3. Train the agent with accurate, structured knowledge sources

What’s an agent in AI without good data behind it? Feed it your help center articles, product docs, and historical ticket resolutions so it has access to all the context and resources it needs. Pylon’s omnichannel support platform gives your AI agents and support team members full account context across all channels, so you can spend less time gathering information and reduce your time to resolution.

4. Customize tone and responses to match your brand

Your AI agent should sound like your brand, so set guardrails for what it can and can’t say. If your company doesn’t discuss enterprise pricing with prospective customers before a sales meeting, for example, your agent shouldn’t, either. Help it match your tone by uploading your support style guide as part of the training data.

5. Test, monitor performance, and continuously optimize accuracy

Once you feel confident about your AI agent, launch it in a small percentage of conversations. Review the chat summaries as soon as conversations end. Look for places where the agent gave the wrong answer, missed an escalation trigger, or confused the customer. After you fix those errors, expand its support coverage and continue the process until you’re completely happy with its work.

Training AI agents with the right data

The difference between an AI agent that frustrates customers and one that resolves issues and improves common customer support metrics comes down to training data. 

Historical support tickets are the most valuable source of information for an AI agent. They show how your team actually solves problems instead of making the agent scan documentation to find the solution. Chat and email summaries give the agent examples of real customer language, including the way customers describe problems when they don’t know the technical term for what’s wrong.

Knowledge base articles and product documentation are a large part of the foundation for automated customer support. And resolution data and customer feedback tell the agent which answers worked and which ones led to follow-up tickets or frustration. The combination of these sources creates an agent that can handle the full range of questions your team sees daily.

Pro tip: Stale data creates stale answers. If your product shipped a major update last quarter but your training data doesn’t reflect that, your AI agent will confidently give wrong answers. Build a process for keeping your training data current and implement it regularly.

Designing effective conversation flows

Conversation flow matters as much as answer accuracy. Almost two-thirds of consumers report they’re more likely to trust AI agents in conversation when they exhibit human-like traits including friendliness and empathy.

Start by mapping the customer journey for each use case. For instance, a customer troubleshooting an integration is at a different stage than one asking about their account settings. Each path needs its own flow with clear decision points.

While natural language processing (NLP) handles finer details of interpretation, you still need to define intents and entities clearly. An intent like “cancel my account” and “pause my account” might sound similar to an agent, but they require different responses. Build those subtle distinctions into your conversational AI runbooks.

When a customer says “that didn’t help” or rephrases their question, the agent needs to reanalyze the question and adjust their answer accordingly. It’s incredibly frustrating for customers when an agent keeps repeating the same answer in different words without actually trying to solve their problem, and it can hurt their trust in your company.

Personalize responses when possible and use customer data to your advantage. If an enterprise account reaches out, use an opener specific to their account context. And when your customers need to speak with a team member, the transition should feel like they’re being introduced to a colleague who already knows the situation.

Monitoring and optimizing agent performance

To see key KPIs improve, you’ll want to regularly optimize your agent’s system:

• Track the right KPIs. Resolution rate, average response time, and escalation frequency tell you whether the agent is helping or creating more work for your team. Also consider containment rate, or the number of conversations resolved without human involvement, and customer satisfaction scores (CSATs) to see what your customers think.
• Run A/B tests on flows and prompts. Change one variable at a time and let the data tell you what works. For example, you can test whether a different opening prompt improves containment. Later, test whether a shorter escalation path improves CSAT without affecting time to resolution. Keep tests separate to avoid confusing results.
• Collect feedback after every interaction. A one-question CSAT survey after each conversation will give you a high-level view of general customer satisfaction. Pair those answers with qualitative reviews of low-rated conversations to find specific failure points.
• Iterate continuously. Your AI agent needs to change with your product to stay up-to-date with customers’ questions. Set a regular cadence for reviewing conversation logs, updating knowledge sources, and retraining.

Scaling conversational support with AI agents

The case for AI agents for businesses is straightforward: Your ticket volume will keep growing as your company onboards more customers, and your headcount likely can’t grow at the same rate. AI agents let you close that gap without sacrificing the quality of your customer experience. And the easiest way to integrate this type of AI agent is with an omnichannel support platform like Pylon.

Pylon is the modern B2B support platform that offers true omnichannel support across Slack, Teams, email, chat, ticket forms, and more. Our AI Agents and Assistants automate busywork and reduce response times. Plus, with Account Intelligence that unifies scattered customer signals to calculate health scores and identify churn risk, we're built for customer success at scale.

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FAQ

What’s an AI agent in conversational support?

An AI agent in conversational support is a system that uses NLP and machine learning to handle customer inquiries through chat or messaging channels, automate responses, and assist your support team when needed.

How do AI agents improve customer support efficiency?

AI agents improve efficiency by handling repetitive questions, providing instant responses, routing complex issues to the right team, and operating 24/7 without increasing headcount.

What data is needed to train an AI support agent?

AI support agents are typically trained using historical support tickets, chat and email summaries, help center articles, product documentation, and resolution data to ensure accurate and relevant responses.