Shaping the Conversation: How We Built Our AI Voice Agent from a Product Lens

When building a product that speaks, the challenge isn’t just in the tech — it’s in the experience. As a Product Manager working on our AI Voice Agent, my role sits at the crossroads of UX, engineering, NLP, and user needs.

Voice products don’t have screens to guide the user step by step. So, the product strategy must prioritize clarity, emotional intelligence, and trust — all without a visible UI. In this blog, I’ll share how we shaped the voice experience from a product perspective, from defining success metrics to aligning cross-functional teams around one shared goal: natural, human-like conversations at scale.

🎯 Framing the Problem
Before any design or development began, we asked ourselves:

• What is the core job of the voice agent?

• In what contexts will users interact with it?

• What would success look like — for the business and for the user?

We narrowed it down to three pillars:

1. Frictionless conversations

2. Fail-safe responses

3. User trust & adoption

These pillars helped shape our product decisions — from fallback handling to voice tone and response time.

🧩 Mapping the Ecosystem
Voice isn’t standalone. We had to map:

• Entry and exit points: Where does the voice agent fit in the larger user journey?

• Connected services: CRM, backend APIs, NLP engines

• Feedback loops: How do we measure and learn from each interaction?

We collaborated closely with designers and engineers to sketch out multi-modal user flows — where voice, visual cues, and real-time system actions worked in sync.

🛠️ Balancing Complexity & Clarity
Voice agents sit on top of very complex systems — natural language processing, intent recognition, session memory. But users don’t care how smart it is. They care if it works.

So our product decisions focused on simplifying:

• Edge case handling: What happens if the user whispers, mumbles, or pauses?

• Agent memory: What should the AI remember in a session? What should it forget?

• Tone calibration: Too robotic? Too casual? We found the sweet spot with A/B testing.

📊 Metrics That Matter
We tracked a blend of quantitative and qualitative metrics:

• Completion rate of conversations

• Drop-off points and recovery attempts

• Time-to-first-response

• User sentiment (via CSAT and open feedback)

These gave us a real-world view into how the agent performed — and what needed fine-tuning.

🤝 Cross-Functional Collaboration
Voice products can’t be built in silos. Every week, I aligned:

• With designers on microcopy, conversation flows, and emotional tone

• With engineers on voice API behavior, response time, and fallback logic

• With data teams on user behavior insights

• With marketing on messaging and onboarding clarity

The key to our success? Making sure everyone could empathize with the user.

🔄 Iterating Through Real Conversations
Voice products live and breathe on real usage. We constantly tested:

• Real-world accents and pacing

• Unexpected interruptions (background noise, switching topics)

• Repeat users vs. first-timers

Each insight helped refine the product — whether it was an updated prompt, a shorter pause, or a redesigned onboarding script.

🔚 Conclusion
As a PM, building a voice agent meant stepping into the user’s shoes — without a screen to stand on. It required deep collaboration, ruthless prioritization, and constant iteration. But most of all, it meant building a product that doesn’t just talk — it connects.

Because when done right, users stop seeing it as software. They start hearing it as a partner.