Top 8 Mistakes to Avoid When Creating an AI Voice Agent

A high-performance AI voice agent is a powerful asset for automating interactions and enhancing customer experience. However, every detail matters to ensure it functions correctly.

Small configuration errors can degrade performance, lead to incoherent responses, or even cause hallucinations.

💡 What is an AI hallucination? It occurs when the agent fabricates an incorrect or off-topic response

Example: An AI agent designed to schedule appointments suggests a service that doesn’t exist. The result? Frustrated customers and a loss of trust.

At Rounded, we want your voice agents to reach their full potential. Here’s a guide to the most common mistakes to avoid.

❌ 1. Not Testing the Agent Thoroughly

An agent may seem well-configured in theory, but each interaction is unique in practice.

✅ Why is this a problem?

• Undetected errors can slip through without thorough testing.

• The agent may struggle with specific scenarios (repetitions, misunderstandings, logical errors).

• It may also mispronounce key terms (e.g., a brand name, a partner’s name, or a date).

• Poor testing can give a false sense of reliability.

🎯 Solution:

• Perform varied test calls, including ambiguous requests.

• Simulate extreme scenarios to see how the agent reacts.

• Iterate and adjust prompts after each test.

❌ 2. Failing to Properly Configure General Settings

The General Settings are the foundation of your agent. A single misconfiguration can completely alter its behavior.

✅ Why is this a problem?

• Choosing the wrong LLM can generate responses that are too long or inappropriate.

• Selecting an unsuitable voice may not match your brand’s tone.

• A vague base prompt can make the agent too generic or imprecise.

• A poorly configured transcriber can lead to interpretation errors.

🎯 Solution:

• Choose an LLM suited to your needs (precision vs. speed).

• Define a clear base prompt, specifying the agent’s role, tone, and rules.

• Personalize the voice to match your target audience.

• Test the transcriber in a telephony environment:

  • Azure is the most reliable in French.

  • Deepgram is much faster and works well in English, but not in French.

📚 See how to configure General Settings in the documentation.

❌ 3. Poorly Structuring Prompts

A well-structured prompt helps guide the AI and prevents it from going off track.

✅ Why is this a problem?

• A poorly structured prompt can cause vague or overly generic responses.

• The agent may deliver incorrect or irrelevant information.

🎯 Solution:
Follow this 4-part structure for prompts:

1️⃣ Objective → Clearly define what the agent should accomplish.
2️⃣ Instructions → Explain how it should respond (tone, format, etc.).
3️⃣ What it should do → Key points it must include.
4️⃣ What it should NOT do → Exclusions and errors to avoid.

Example:

"You are an assistant responsible for scheduling medical appointments. You must offer an available slot and confirm with the caller. Never provide medical advice."

To avoid overloading prompts, we recommend storing key documents in the Knowledge Base.

❌ 4. Using Too Many Tools in a Single Task

✅ Why is this a problem?

• Each tool used in a task adds an extra layer of complexity.

• Too many tools can slow down the agent’s response and create execution conflicts.

🎯 Solution:

• Limit each task to 3 or 4 tools max to avoid overload.

• Break tasks into smaller, separate steps for better performance and stability.

• Test tool integrations carefully before deployment.

📚 See how to declare and configure tools in the documentation.

❌ 5. Poor Handling of Misunderstandings

AI does not always perfectly understand every request. Anticipating misunderstandings is essential.

✅ Why is this a problem?

• Without a clear fallback strategy, the agent might repeat errors in a loop.

• Instead of acknowledging confusion, it may give a misleading response.

🎯 Solution:

• Implement fallback responses (e.g., "I didn’t quite understand. Could you rephrase?").

• Allow users to go back and clarify their request.

• Test how the agent reacts to ambiguous phrases.

❌ 6. Poor Variable Management

✅ Why is this a problem?

• A misnamed or incorrectly retrieved variable can distort responses.

• If the agent needs to use caller data (name, date, phone number) but variables are incorrectly defined, it may not function properly.

🎯 Solution:

• Use clear and explicit variable names (e.g., client_name, appointment_date).

• Debug and test each variable to ensure proper transmission.

📚 See how to declare and configure variables in the documentation.

❌ 7. Poor Task Flow and Connection Between Steps

✅ Why is this a problem?

• If a user wants to go back to a previous step, the agent might lose track of the conversation.

• A poor task flow can block interactions or cause unnecessary repetitions.

🎯 Solution:

• Enable backward navigation so users can clarify or correct information.

• Test the flow between tasks to ensure seamless interactions.

❌ 8. Incorrectly Formatting the CSV File for Call Campaigns

✅ Why is this a problem?

• If you launch an automated call campaign, the agent retrieves numbers from a CSV file.

• A poorly formatted CSV can block the campaign.

• Common mistakes:

  • Missing phone_number column.

  • Incorrect number formatting (e.g., 06... instead of 33 6...).

🎯 Solution:

• Verify the CSV file format before import.

• Test a small sample before launching a full campaign.

• Always use ISO-format phone numbers to ensure compatibility with call automation.

Click here to download a CSV template.

📌 Conclusion

You now have all the key insights to build a high-performance AI voice agent while avoiding common pitfalls. By carefully configuring prompts, optimizing workflows, and testing extensively, you can ensure your agent is reliable, efficient, and user-friendly.

🚀 Ready to bring your AI voice agent to life? Start building today on the Rounded platform and take your automation to the next level!