7 Tips for Building AI Agents That Perform

When building AI Agents, it’s helpful to think of them more like humans than code. Code follows explicit instructions and fails when edge cases aren’t handled, yet it performs reliably and predictably. You can typically trace exactly why the code behaves a certain way. On the other hand, Generative AI is non-deterministic, meaning it generates different outputs from the same input. These models use neural networks to identify patterns within data and produce novel content. This behavior closely mirrors how the human brain operates—constantly adapting, learning, and responding in unique ways to similar stimuli.

To create high-performing AI Phone Agents capable of achieving human-like results, you need to craft prompts in a way that mirrors how you would guide human agents. 

Proven Tips for Building AI Agents That Perform Like Pros

1. They Need a Persona

Like humans, AI Agents need a personality in order to understand how to respond.

Examples:

• You are a customer service agent who is understanding, friendly and helpful. 
• You are a sales rep who is pushy, but respectful and honest. You don’t take hesitation on behalf of the prospect as a sign of “no”, but rather as a sign that you should take a different approach in positioning the product to address their specific needs.

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2. They Thrive on Clear Goals

Before diving into specific conversation flows or edge cases, establish the high-level goals for your AI agent. Clearly outline what you want it to accomplish and the actions it should take. This provides a framework for more detailed instructions later on.

Example:

• Goal: You are an AI agent for Circle Life – a life insurance company – qualifying leads. Your job is to ask questions to determine if a prospect meets the company’s minimum criteria. If they qualify, you will transfer them to a licensed agent. If not, you’ll politely end the call. This process helps save licensed agents' time by only transferring qualified leads.

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3. They Need Structure

Just like humans thrive with clear outlines, building AI Agents requires giving them structured prompts to allow them to perform effectively. Organizing your instructions into sections, using bullet points and lists, helps the AI process the information in a logical way.

An effective outline might look like this:

• Persona:
• Goal:
• Task:
  • Greeting:
  • Qualification Questions:
    • XYZ
    • ABC
    • DEF
  • Objection Handling:
    • Objection: [Specific objection]
    • Handling: [How to address it]
  • Action: [Next steps]
  • Closing: [End of the conversation]
• Don’t Do:

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4. They Get Overwhelmed by Too Many Instructions & Tasks

The more instructions you provide to while building AI agents and the more tasks you expect it to perform in a single call, the more likely the AI agent is to become confused and less performant. Shorter and more focused prompts allow AI agents to process information and respond more accurately. 

This may require you to start to break up your call center interactions into smaller, more focused interactions, rather than longer, multi purpose interactions. 

For example, if a customer has a billing issue, you may route them to an AI Agent specifically focused on the task of billing, such as listening to the customer’s issue, pulling up their billing statements, and working out the math to determine if there is in fact an issue, then issuing a refund or not. 

However, if the customer then says, “thanks and now I also have a troubleshooting question and need help with set up”, then it’s likely better to “transfer” to a separate AI Agent who is specifically focused on product installation issues. Like humans, AI Agents perform better when they are specialized, but unlike humans, transfering takes no time, since AI Agents are infinitely available. 

5. They Learn from Examples

Rather than simply telling AI agents what to do, give them real-world examples to follow. Humans learn best this way, and so do AI Phone Agents. Provide both good and bad examples so they can distinguish between the two.

For example:

• Good Example:
  • Customer: "Can’t you just tell me the price? You’re wasting my time."
  • Agent: "I understand this can be frustrating. Answering these questions will help me find the best options for you."
• Bad Example:
  • Customer: "Can’t you just tell me the price? You’re wasting my time."
  • Agent: "By answering these questions, I can find the best options for you."

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The bad example fails to acknowledge the customer’s frustration, whereas the good example addresses it before moving the conversation forward.

6. They Need Boundaries

It’s important to set boundaries for when AI agents should stick to the script versus when they can improvise. Establish clear rules about what they can and can’t do.

For example:

• If a customer asks for legal or financial advice, the AI should not provide any and instead transfer the call to a licensed advisor.
• If the customer engages in small talk, the AI is free to engage in a friendly manner.
• If the customer asks to update their profile, the AI should say it’s not authorized to make those changes.
• If the customer asks if it’s an AI, the AI should answer truthfully.

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7. They Get Better If They Have More Time to “Think”

This is less applicable to AI Phone Agents for whom speed is of the utmost importance given they are on live calls, but for AI Agents communicating over digital channels like SMS, Chat or Email, allowing the agent to plan out their response, generate a draft, and then self-reflect and edit that draft before sending the final response will lead to better results. Sound familiar? In other words, agents can self-critique and iteratively improve, if given the time. This idea is at the heart of “Agentic Workflows”, but it’s not always practical if there are other constraints such as latency.

Who Should Build and Manage AI Agents?

AI Phone Agents, while far from human, are much more than static code. By treating them like you would human agents—providing clear goals, structured tasks, examples, and feedback—they become powerful tools capable of handling increasingly complex interactions. 

This raises the question: who in your call center should be responsible for building, monitoring and managing AI Agents? 

We believe that instead of creating a net new role or handing off the responsibility to an engineer or product manager, it should become the job of the contact center manager. The person who today already supervises the agents, monitors their performance, listens to their calls, and gives them coaching points. That’s exactly what’s required to build and manage effective AI Agents. 

As for any of the technical pieces, choosing the right platform to build your AI Phone Agents will abstract away the need for any technical knowledge around LLM choice, RAGs, Functions, Agentic Workflows, etc., letting the contact center manager do what they already do best – just with agents who learn exponentially faster!

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