Real-time conversational agents need to remove the friction introduced by latency in order for them to feel truly human.The key to minimizing call abandonment while maximizing user trust is enabling a response time that comes in below the psychological threshold of 130 milliseconds for Time-to-First-Audio, or TTFA.
This guide will walk you through how to create low-latency voice agents with Falcon, which uses its architectural advantages to consistently deliver a lightning-fast, seamless conversational experience. By focusing on smart API integration, pipelined processing, and deployment strategy, developers can bypass common performance bottlenecks from classic TTS systems and deploy a voice agent suited for real-time, high-volume B2B interactions.
The 4 Phases of Building a Sub-130-ms Voice Agent with Falcon
Are you ready to know how to build a sub-130-ms voice agent with Murf Falcon? We have broken it into four phases. Let’s go.
Phase 1: Foundational Setup and API Integration
The first step in achieving sub-130-ms performance is picking the right foundational component, which in this case is Murf Falcon. Falcon is engineered for speed, with a model latency as low as 55 ms and a typical Time-to-First-Audio (TTFA) of about 130 ms. The key to this is not custom code, but correct integration.
- Choose the Right SDK/Endpoint
Where possible, to reduce integration overhead and complexity, utilize the provided SDKs (e.g., Python) or the dedicated RESTful API endpoints.
To achieve the lowest latency, the system needs to be set up for streaming output.
Unlike older batch APIs that wait until the entire text is processed before sending any audio, Falcon’s ability to stream sends audio data back in chunks the moment the first phoneme is synthesized. It is this overlapping process that is crucial for cutting down perceived latency.
- Establish a Continuous Connection (WebSockets)
For the most demanding real-time applications, such as a contact center or a live qualification bot, a dedicated, continuous WebSocket connection is preferred over repeated REST API calls.
More complicated to set up, but WebSockets eliminate the overhead of repeated TLS handshakes and connection establishment. This will shave precious milliseconds off every single turn of the conversation. This continuous stream forms an essential ingredient in how to build low-latency voice agents using Falcon that maintain performance at scale.
Phase 2: Architectural Pipelining for Speed
This sub-130-ms goal is architectural, not just a software problem. In essence, this depends on the simultaneous processing of the three cornerstones that make up a voice agent: ASR, NLU, and TTS.
- Implement Streaming ASR and Overlap
Your ASR component, which transcribes the customer’s speech to text, must also be streaming. This means it transcribes the user’s speech when they are still talking. The partial transcript is immediately fed to the NLU/LLM component. The NLU starts to create a response based on the partial text. The overlap allows the system to begin synthesizing the TTS response via the Falcon API before the user even finishes their sentence. This is the main technique for achieving a TTFA below 200 ms.
- Focus on Response Generation
The text to be passed to the Falcon API should be concise and direct. Although Falcon is highly expressive, refrain from sending huge blocks of text because the system needs to process the full input before it can start synthesis.
The rule of thumb is to design conversational turns to be short, targeted responses instead of long monologues; for example, “Yes, I can help with that. Are you calling about a new account?” This will ensure that the TTS engine receives the text more quickly and is able to return audio sooner.
Phase 3: Deployment and Network Optimization
Even the fastest TTS model can be crippled by slow network transit. Falcon’s architecture explicitly addresses this through edge deployment.
- Leverage Edge Deployment and Data Residency
Falcon is deployed globally over multiple edge locations. When configuring your agent, make sure your application server and the Falcon real-time voice agent API are physically located in the closest possible geographic region to your end-users.
The implication of using local edge deployment is that the hop times are minimized, enabling drastic reductions in latency variability. Thus, it ensures that the 130 ms TTFA benchmark is achieved across various countries and not just in a lab environment.
- LLM/NLU Component Optimization
While Falcon does handle TTS output, the overall performance of your voice agent still depends on either the Large Language Model (LLM) or the NLU layer.
The best practice is to utilize a lightweight or highly optimized LLM to perform the conversational logic (or fine-tune a model). A complex and huge model will add latency that will offset most of the speed advantages of the Falcon TTS. Full parallel processing is best, which means the TTS component never needs to wait for very long on the text input.
Phase 4: Monitoring and Continuous Improvement
Building an agent of less than 130 ms is a continuous process, which involves much vigilance and calibration.
- Monitor TTFA and Latency Consistency
Implement real-time monitoring to track the true TTFA of your agent, not just the model latency: Key metrics to track:
- Average TTFA: This should be below target (for example, 130 ms).
- P95 Latency: This is the latency for 95% of your calls. This metric is far more indicative of the customer experience than the average. A high P95 indicates inconsistent performance that should be investigated for potential improvements via better load balancing or edge distribution.
By strategically integrating Falcon’s highly efficient core and optimizing the surrounding ASR and NLU components for streaming, you move beyond simply automating calls to deploying a sophisticated conversational tool that maintains the rhythm and trust of a human interaction and ensures that your sub-130-ms goal is not an aspiration but a production reality.
