After building an autonomous voice agent prototype, I learned the real product isn't voice—it's trust. Here's what that means for the future of contact centers.
When I set out to build my very randomly and unrelatedly named agent, VERA (Versatile Experiential Response Agent), my initial goal was technical: I wanted to see how far I could push OpenAI’s Realtime API to create a seamless voice assistant. I expected the challenge to be in the code—handling WebSockets, managing buffers, and stringing together API calls.
But as the agent evolved from a prototype into a production-ready system, the real challenge wasn't just making it talk. It was making it trustworthy. After hundreds of hours refining this prototype, I learned that the "voice" is just the interface. The real product is trust. Here's what building this voice agent taught me about the architecture of reliability, the nuance of human conversation, and the future of the contact center.
Key Takeaways
- Latency is an emotional metric, not just a performance stat. Three seconds feels broken. Nanosecond responses feel robotic. Trust lives in the breathing room between.
- Voice needs dual input modes. Open mic for speed, push-to-talk for psychological safety when users need time to think.
- Multi-modal wins. Voice handles the relationship; screens handle verification. Pure audio experiences fail when users need to trust what they're hearing.
- Treat AI agents like junior employees. Guardrails, tool use, and traces aren't optional—they're how you build accountability into probabilistic systems.
Latency is a Trust Metric
In the world of voice AI, we often treat latency as a performance stat—something to optimize for efficiency. But for the user, latency is an emotional metric. In a contact center environment, this is where the quality of service (QoS) is truly measured.
If an agent takes three seconds to respond to "Hello," the customer assumes the line is dead or the system is broken. If it cuts them off the nanosecond they finish a sentence, it feels robotic and aggressive. Delays erode trust, but hyper-speed kills comfort. For contact centers, getting this balance right is the difference between a satisfied customer and a frustrated hang-up.
This is why architecture matters. Early in development, I realized that standard WebSocket implementations often struggled with the fluid interruptibility required for a natural conversation. By shifting the prototype to WebRTC, I unlocked the low-latency capabilities necessary for a "living" dialogue. WebRTC allows the conversation to breathe, enabling the user to interrupt the agent mid-sentence—a critical requirement for high-stakes service interactions where customers need to clarify points in real-time.
pc.ontrack = (event) => {
audioRef.current.srcObject = event.streams[0];
audioRef.current.play();
};
However, speed is a double-edged sword. You need a Voice Activity Detection (VAD) system that is sophisticated enough to distinguish between a user who is finished speaking and a user who is just thinking. I consider VAD tuning to be a critical KPI for any voice agent. If the turn-detection is too aggressive, the user feels rushed. If it's too loose, the user wonders if the line went dead.
case 'input_audio_buffer.speech_stopped':
if (inputMode === 'open_mic') setTimeout(() => {
sendEvent({ type: 'response.create' })
}, 1200); // delay that breathes
The Case for Dual Input: Meeting Diverse Customer Needs
Even with the most generous VAD settings, sometimes customers just need time to think or look up a reference number.
In testing, I found that relying solely on voice detection failed in complex scenarios. When customers are formulating a complex question or searching for a bill while talking, they naturally pause. A purely VAD-based agent interprets that silence as "my turn" and jumps in, interrupting the user's train of thought.
To solve this, I implemented a Dual Input Architecture:
- Open Mic (VAD): For quick, back-and-forth banter and simple inquiries.
- Push-to-Talk (PTT): A manual override that gives the customer total control over the interaction.
Implementing PTT wasn't a regression; it was a UX breakthrough for contact center reach. This customizable nature of interaction ensures a farther reach for customers with different communication preferences. Whether a user is in a noisy environment or simply prefers the deliberate pace of a button-press, PTT provides the psychological safety to take their time, knowing the agent won't interrupt until they are ready.
It bridges the gap between the speed of AI and the sometimes meandering pace of human thought.
"Trust but Verify": Bridging Voice and Visuals
We often romanticize the idea of a "Voice-Only" future. I started building this agent with that same vision—a screenless, pure audio experience.
I was wrong.
While voice is excellent for speed and ease, it is terrible for verification. If the agent answers a complex financial question or retrieves a specific fact, how does the customer know it's true? Blind trust in an LLM is a recipe for hallucination.
I pivoted the prototype to a Hybrid Interface. Now, as the agent speaks, a real-time transcript appears on the screen. More importantly, when the agent uses its web-search tools, it displays citations and clickable links. For a contact center, this hybrid approach is vital; it allows the voice agent to handle the relationship while the screen handles the hard data, ensuring the customer feels both heard and informed.
The prototype displaying web-search results and citations in real-time during a conversation about stablecoin regulation
This visual feedback loop serves two purposes:
- Grounding: It anchors the conversation, allowing users to verify facts (like citations or math) instantly.
- Escalation: It provides a transcript history that can be audited later.
Multi-Modal Reality
The most effective voice agents won't be invisible; they will be multi-modal, using voice for the relationship and screens for the details.
Guardrails Over Magic
Relying solely on an LLM to handle everything—from banter to business logic—is dangerous. LLMs are probabilistic, not deterministic. They are great storytellers but unreliable accountants.
Building a production-ready agent required strict Guardrails and Tool Use. I couldn't just let the model "guess" the answer to a search query or a calculation. I had to architect the system to rely on specific tools (like OpenAI's web_search) and audit those interactions using traces.
For those unfamiliar, traces act as a transparency layer for the AI. They don't just record the output; they capture the model's internal logic, showing us exactly when it decided to trigger a tool, what arguments it passed, and how it interpreted the results. This transforms the agent from a "black box" into a debuggable workflow.
Treat AI Like Junior Employees
We need to treat these agents less like magic boxes and more like junior employees. We need to monitor their "work"—auditing whether they called the right tool, if they stayed within policy, and if they escalated when confused.
Auditing traces allows us to see exactly when an agent complied with policy (e.g., using a calculator tool instead of guessing) and when it failed. This feedback loop allows us to continuously refine the "employee."
The Future State: Automating Execution, Not Relationships
There is a fear that Voice AI will replace the contact center. I don't see that happening. Instead, I see a future where Voice Agents scale the contact center.
In this future state, human agents will graduate from answering repetitive calls to becoming "Agent Managers." Instead of handling one customer at a time, a human might oversee a suite of ten AI agents.
- The AI handles the execution: data retrieval, transaction processing, and initial triage.
- The Human handles the relationship: monitoring sentiment, intervening when an agent gets stuck, and grading the agent's performance to improve future interactions.
Why It Matters
Building this agent taught me that the goal of AI isn't to remove the human from the loop. It's to elevate the human to the top of the loop. By letting the AI handle the execution, we free up the humans to manage the quality, the strategy, and ultimately, the relationship.
Architecture Blueprint: The Hybrid Model
This architecture of accountability enables a new future. Below is the architecture that makes this possible—not a chatbot, but a voice operating system for service:
- Customer Audio → Realtime API (low-latency streaming)
- AI Layer → NLU + supervised tools + ephemeral credentials
- Orchestration Layer → state machine for escalation + handoff
- Tooling Layer → calculations, account lookup, policy guardrails
- Agent Assist UI for humans → "next best action" + call context
Conclusion
This prototype isn't finished—but the architecture it proved is the starting line for the next era of contact centers. We are entering an era where AI agents will become the frontline of customer interaction. But the real product isn't the voice interface. It's the trust we build into every architectural decision.
If you're building for this horizon, don't ask: "How do we deploy AI?" Ask: "What do we want our AI to be accountable for?" Building this agent taught me that automating execution without automating relationships is how we build AI worth trusting in the contact centers of tomorrow.
