AI agents aren’t plug-and-play solutions. Think about it: You wouldn’t hire a new support agent and expect them to operate at full capacity without training, right? Just like human employees, you need to coach AI continuously to reach peak performance.

That’s where AI coaching comes in—the process of refining your AI’s ability to handle more complex inquiries, personalize interactions, and align with your customer service goals.

After years of working as an AI manager and supporting AI managers at other companies, I can confidently say: the best AI agents aren’t the ones with the most data—they’re the ones that are coached on how to best use it. A well-coached AI agent is so much more than your run of the mill “chatbot”—it’s a digital team member that learns, adapts, and improves over time.

But where do you start? If you want an AI agent that delivers seamless, human-like customer experiences—handling complex inquiries, resolving issues accurately, and adapting in real time—you need to approach coaching with intention. Here’s how to set your AI agent up for success.

1. optimize your knowledge base for AI consumption

Your AI is only as good as the data it learns from. That’s why your AI agent’s first teacher is your knowledge base —but not all knowledge bases are AI-friendly.

In my past role as an AI manager, I found that there are types of content that work well for human consumption that don’t work for AI. While a human can easily scan a table or an infographic for relevant data points, it’s difficult for AI to retrieve the right answers.

But don’t go scrapping the content altogether— there’s a better solution . Here’s how you can reformat your existing information in a way that AI can understand:

• Audit your knowledge base: Identify articles that rely too heavily on complex formatting, tables, or non-text elements like images and infographics that AI may struggle to interpret.
• Convert and structure content for AI: Reformat key information into structured, AI-digestible text by breaking down tables, summarizing visual elements, and ensuring content follows a clear, logical flow.
• Create AI-specific knowledge resources: Develop internal articles or FAQ entries tailored specifically for the AI agent, focusing on clear, concise answers that align with customer queries. Provide additional information that your human agents would know but isn’t available in the public help center, such as internal processes, edge case handling, or policy exceptions.

If you do this, you will see immediate improvements in response accuracy. Training your knowledge base to be AI-friendly is the foundation of effective coaching.

2. use AI coaching tools to fine-tune responses

Even with a solid knowledge base, your AI agent won’t get every response right the first time. And constantly updating the knowledge base to correct minor AI mistakes isn’t scalable.

Instead, use AI coaching tools to refine responses at the conversation level. This ensures that the AI understands context, learns from past mistakes, and improves dynamically. Instead of rewriting your entire knowledge base every time something isn’t working, try the below:

• Set coaching rules: Adjust AI responses in real-time by teaching it how to prioritize specific knowledge sources or refine how it answers certain inquiries.
• Review AI performance trends: Use analytics to identify patterns where AI misinterprets customer intent or escalates cases unnecessarily. Focus coaching efforts where AI struggles most.
• Implement feedback loops: Continuously refine AI’s responses by monitoring customer sentiment and escalation data. Make iterative adjustments to improve AI accuracy over time.

It’s easy to get caught up in fixing individual misfires, but AI coaching is most effective when it’s guided by patterns.

Instead of trying to "fix" AI every time it makes a mistake, start setting coaching rules that adjust the AI’s response based on real interactions. This way, you can employ precise, targeted improvements across a broader range of conversations—and ensure that corrections apply only where needed.

3. leverage customer data to personalize AI responses

A great AI agent doesn’t just provide accurate answers—it provides personalized ones.

Accuracy alone isn’t enough. Customers don’t want to interact with AI that is blatantly robotic and impersonal, and generic AI responses often lead to frustrated customers and escalations.

To create a human-like, AI customer experience, coach AI agents to be able to pull in customer-specific context, like purchase history, past interactions, and customer sentiment. To do this, AI managers should:

• Integrate AI with your CRM: Ensure AI has access to customer order history, past interactions, and account details to provide personalized support.
• Use sentiment analysis: Train AI to adjust its tone based on customer mood—whether they’re frustrated, neutral, or positive. AI should offer empathetic, proactive support when needed.
• Segment responses by customer type: High-value customers, first-time buyers, and returning users may have different needs. AI should be trained to recognize user segments and adjust responses accordingly.

By embedding customer data into AI conversations, businesses can create more meaningful, relevant interactions—reducing escalations and improving satisfaction. For example, if a repeat customer reaches out about a delayed order, the AI should recognize them, acknowledge their previous purchase, and provide an update specific to their order rather than delivering a generic shipping policy.

Don’t let your AI operate in a vacuum. Connect it to customer data so it can offer relevant, contextual responses.

4. identify and address coaching blind spots

One of the biggest mistakes an AI manager can make is to only focus on the most common inquiries. It’s tempting to assume that if AI gets the top 10 FAQs right, then it’s working well. But not all AI failures are obvious.

In reality, AI failures tend to happen in edge cases, not the easy stuff. Testing beyond top inquiries is crucial—some of the biggest AI failures happen in complicated workflows, not the most frequent tickets​.

AI managers should take a proactive approach to coach AI by identifying weak spots and course-correcting before issues escalate by:

• Testing beyond common FAQs: Don’t just evaluate AI on the most frequent inquiries—test it with multi-step, complex, or ambiguous customer questions to reveal weak points.
• Analyzing unresolved cases: Look at AI escalation data and drop-off points to identify where customers abandon conversations or seek human assistance.
• Implementing structured scenario testing: Regularly test edge cases, vague inquiries, and multi-intent messages to see where AI struggles and refine coaching accordingly.

The only way to catch these failures is to test for them intentionally. Instead of only evaluating how AI handles frequent, simple questions, AI managers should proactively test it with multi-step, complex inquiries.

Looking at AI analytics reports can also reveal hidden issues—if certain inquiries are being escalated at a high rate, that’s a sign the AI needs additional coaching in that area.

5. avoid micromanaging: balance AI autonomy with coaching oversight

AI agents aren’t static scripts—they are designed to learn and adapt. One of the biggest mistakes AI managers make is overcoaching—constantly tweaking AI responses to "perfect" every interaction.

When too many coaching rules are applied, they can contradict each other, creating unpredictable results—aka, making the AI’s behavior worse.

The goal is to coach AI strategically, not reactively, with a clear strategy, focused on the right areas, and given room to adapt within reasonable guardrails. Here’s how you find the right balance:

• Define coaching priorities: Identify high-impact areas (e.g., refund policies, security-related questions) where AI must always be accurate—but allow for flexibility in lower-risk interactions.
• Avoid reactionary coaching: Instead of fixing every one-off mistake, focus on long-term trends. If AI gets 90% of responses right, don’t overcorrect for the 10% anomaly.
• Limit conflicting coaching rules: Overloading AI with too many specific coaching rules can reduce performance. Keep AI coaching streamlined and focused on essential improvements.

AI coaching should be targeted and intentional, ensuring steady improvements without micromanaging the AI’s learning process. Ask yourself: Is this a high-risk topic that requires strict control, or is it okay for AI to adapt naturally?

AI coaching is an ongoing process

A well-coached AI agent doesn’t just provide answers—it understands context, adapts to customer needs, and continuously improves. AI managers who take a structured, data-driven approach to coaching will see:

• Higher resolution rates and reduced escalations.
• More personalized, customer-centric AI interactions.
• Improved CSAT scores and operational efficiency.

Again, coaching isn’t about fixing every mistake—it’s about guiding AI toward better decision-making over time. A well-coached AI agent doesn’t just answer questions—it understands, adapts, and improves. This means coaching with intention, monitoring consistently, and refining strategically.

AI coaching is the secret to AI-driven customer experiences that feel human. Start coaching today, and watch your AI agent evolve.