Voice Agents: the good, the bad, and the ugly

Summary

In this insightful discussion, Eddie Seagull, CTO at Fractional AI, explores the challenges and strategies behind developing AI-powered voice agents for conducting human-like interviews. The conversation highlights the complexities of handling transcription, latency, and evaluation in real-time AI systems. Key approaches, such as using modular agents for task management and out-of-band checks for behavior control, are discussed. The importance of continuous evaluation, especially in the absence of an objective truth, is emphasized, showcasing the iterative process of refining AI systems for robustness and scalability.

Takeaways

• 😀 Language models (LLMs) are difficult to work with, requiring significant development effort for real-world applications like voice agents.
• 😀 Evaluating LLMs is challenging due to the absence of objective metrics for performance measurement.
• 😀 Automating interviews with AI voice agents can save time and resources by replacing human consultants conducting in-depth interviews.
• 😀 Tool use and supplementary agents are necessary to guide LLM behavior and ensure proper conversational flow during interviews.
• 😀 Latency and transcription errors can hinder the natural flow of AI voice agents, requiring additional agents for error handling and correction.
• 😀 Drift detection agents help monitor conversations and redirect them back to the intended goals, ensuring the interview stays on track.
• 😀 Adding multiple background agents and clear interview goals improves the AI's ability to stay focused and ask relevant questions.
• 😀 Transcription errors caused by speech recognition models like Whisper can lead to misinterpretation of silence or background noise.
• 😀 Creating synthetic conversations using AI-generated personas helps simulate real-world interviews for testing and performance evaluation.
• 😀 Developing AI agents requires continuous refinement and tuning, especially when building robust and scalable real-world applications.
• 😀 Evals (evaluation metrics) are critical for measuring success and guiding the development of AI agents, even when no objective truth is available.

Q & A

What is the main focus of Eddie Seagull's presentation?

Eddie Seagull's presentation focuses on the challenges and development process of building AI voice agents, particularly for automating tasks such as conducting interviews.

What are some of the unique challenges associated with building voice models compared to traditional language models?

Voice models face additional challenges such as transcription errors, difficulties in streaming environments, and creating fluid, natural conversations, which are not as prominent in text-based models.

What was the primary goal of automating consultant interviews using AI?

The primary goal was to automate qualitative research interviews, which are traditionally performed by consultants, in order to save time, reduce costs, and improve efficiency by conducting multiple interviews simultaneously with automatic transcription.

What issues did the initial system, using OpenAI's real-time API, face?

The initial system faced issues with rigid conversation flow, where the language model would often go off-topic or digress into irrelevant discussions, making it difficult to manage dynamic, fluid conversations.

How did the team address the issue of the AI agent going off-topic during interviews?

The team introduced a 'drift detector' agent, which monitored the conversation and ensured the interview stayed on track. The agent would trigger a change in behavior if the conversation drifted too far off course.

What role did background agents play in the development process?

Background agents were introduced to monitor and guide the interview process, ensuring that the system's goals and priorities were aligned, and helping to manage the flow of the conversation in real-time.

What problem did the transcription system face and how was it handled?

The transcription system, powered by Whisper, had issues with background noise and silent moments, leading to transcription errors. To address this, the team introduced a new agent to filter out transcription errors and maintain a smooth user experience.

How did the development process become more complex over time?

As the team added more agents to address different issues (e.g., drift detection, transcription errors, and conversation management), the system became more complex. These agents worked in parallel to improve the AI's performance, but also added more layers of coordination and management.

Why are synthetic conversations important in the development of AI agents?

Synthetic conversations, where different personas are simulated, are important for automated testing and evaluation. They allow the team to efficiently assess how the AI handles various scenarios and improve its behavior through targeted adjustments.

What is the significance of using metrics and evaluations in the development of AI voice agents?

Metrics and evaluations are critical in guiding the development of AI voice agents, as they provide feedback on the system's performance. Even without objective truth data, evaluations help identify areas for improvement and track progress throughout the development cycle.