Capable artificial intelligence in customer service can get through repeatable workflows with little or no intervention from a human agent, from investigating billing discrepancies to handling account requests.
Building AI skills for customer support is relatively simple. The challenge is testing and refining a skill until it can reliably handle tasks on its own.
In this guide, we’ll walk through how to build, test, and refine an AI skill. We’ll also cover how to decide what workflows can be automated into skills.
An AI skill is a reusable set of instructions for an AI agent to tackle specific support tasks. Unlike traditional automation that follows fixed rules, AI support agents use natural language processing (NLP) and machine learning to interpret customer requests and generate responses, while the skill defines how the agent handles the task. It establishes how to evaluate a situation, what actions to take, and when to ask a team member for approval.
AI-powered customer support systems can automatically handle routine support requests, like answering FAQs or retrieving order status updates. Complex account issues are different.
Resolving an integration error, for example, might start with reviewing previous support tickets to see if the problem has been solved before. In that case, an AI agent can investigate the account context and recommend a resolution to a team member. They can then use the information to come up with a solution faster than if they’d done the work alone.
Supervised skills let teams automate customer support without giving AI agents and skills control over important decisions. This keeps team members involved when judgment is necessary without sacrificing automation’s efficiency. Without it, the customer could get information that doesn’t fit their account (or information that’s just incorrect), which leads to lost trust.
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Start with one repetitive support task that follows the same rules every time and test how well the AI agent handles customer issues. From there, review the agent’s decisions, correct errors, and gradually expand what it can do autonomously.
Select a B2B support task your team handles regularly with a clearly defined outcome.
Ticket triage is a practical option for an automated support skill. A triage skill could analyze and categorize the ticket’s subject, then group them for the support team based on shared characteristics and urgency.
Other strong candidates for an early skill include flagging accounts with a high churn risk and collecting context and historical ticket data to help team members troubleshoot queries.
An AI skill needs direction to work. For instance, a skill that investigates a bug escalation might need to check product documentation to understand how a feature should work, or analyze the account’s ticket history to see whether the issue has been reported before.
Review all your information before you send it to the skill. An outdated knowledge base article can lead an agent to recommend the wrong troubleshooting steps, which creates more problems than it resolves.
Once you identify and verify the information your skill needs, define the procedures an agent should follow. Check that the agent can retrieve the necessary context and has all the right permissions to perform the outlined actions. Pylon’s Connectors give Skills access to relevant resources across external systems, for instance.
Decide which parts of the skill workflow an AI agent can handle independently and which require supervision. For example, you might instruct the agent to gather relevant error codes for a bug report, while configuring approval controls so a team member reviews the information before deciding whether to escalate it to the engineering team.
The skill should also have rules on what to do when information is missing. If it can’t verify a customer’s account configuration, for example, it should flag the issue for review.
Broader escalation rules belong in the agent’s configuration, not the skill itself. Sentiment analysis might detect a frustrated tone in a ticket, which could instantly trigger a handoff to a human agent regardless of which skill is running.
Before introducing a skill to live tickets, test it against previously resolved issues. Pylon supports backtesting, which lets teams select old support cases and see how an AI agent would handle them during testing.
Look for cases involving incomplete account information and escalations. Can the agent still retrieve the right context? Does it recommend the correct troubleshooting steps? An agent that can’t complete a step might not have the background it needs. Revise the skill’s instructions, then test it again.
Once the skill performs consistently, introduce it to a live workflow with human review of every output. As results improve, gradually reduce supervision for lower-risk actions while keeping team approval in place for the big picture.
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One challenge with AI in customer support is keeping instructions accurate as products, policies, and customer needs change. That takes regularly checking in to measure how the skill performs. A few common mistakes can undermine its effectiveness:
Your first version of a skill won’t get everything right. As your team uses it, you’ll find instructions that need adjusting. Review approval, escalation, and missing context across support tickets. Pylon can suggest improvements to Skills based on their results, which teams can apply and test before launch.
Move on to the next skill only after the skill’s approval and escalation rates hold steady.
As you build out a library of AI skills, your support team’s workload will shift to directing workflows and overseeing queries that need judgement instead of resolving every ticket themselves.
The shift will call for more technical expertise. Someone familiar with your AI system and integrations can troubleshoot problems that support agents may not know how to fix. You may need to hire another team member to specialize in improving those skills and refining your AI agents over time. Otherwise, updates might become reactive as the team adds those responsibilities to their daily support workloads.
Pylon’s first AI skill was built in Claude. It worked, but sharing and keeping it updated was difficult. The experience showed us why skills need to live where customer support work happens, with shared access and a simpler way to maintain them.
Pylon’s Skills let teams use the same instructions across Assist Agent, Support Agent, and Automations. A teammate can run a skill manually, or an AI agent can access it automatically. Skills can call Connectors, which can give those Skills access to your MCP-compatible tools and account context needed to complete the task.
Pylon is the Agentic Support Platform built for B2B support teams. It brings conversations across Slack, Teams, email, chat, and other channels into one place, giving AI the context to help resolve customer issues and continuously improve your knowledge base. Pylon can identify missing or outdated documentation, generate new content from real support conversations, and help your team keep knowledge accurate as your product evolves.
AI agents can resolve repetitive requests, like answering FAQs, and assist team members with complex customer issues, like pulling context for a query and drafting responses.
Chatbots can handle straightforward questions based on pre-set scripts, while AI agents and virtual assistants can investigate tickets, draft responses, and pull information from knowledge bases before handing a query over for human review.
AI agents are best used to resolve high-volume tasks with repeatable processes and clear outcomes, such as ticket triage and escalation.
Agentic AI combines NLP and machine learning to follow defined workflows, requesting human approval when they’re triggered to. In B2B customer support, that looks like analyzing the language in a ticket to find patterns, then matching them with resolved queries or customer context to determine the solution.
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