4 Key Considerations for Building Voice AI Agents: Latency, Accuracy, Costs, Humanity

Building effective AI agents requires balancing four critical pillars: latency, accuracy, costs, and humanity.

• ⚡ Latency: AI agents must respond in real-time to maintain engagement, particularly in high-stakes domains. Optimizing workflows, using resource-efficient models, and reducing system bottlenecks are key to minimizing latency.
• 🎯 Accuracy: Precision and reliability are critical for building trust. Improve accuracy through fine-tuning, confidence scoring, and feedback loops. In specialized applications like medical transcription, solutions like Deepgram’s Nova-2 Medical Model and DeepScribe deliver exceptional accuracy for complex terminology.
• 💰 Costs: Developing scalable and sustainable AI systems requires balancing investment in proprietary tools and open-source solutions while optimizing token usage, integrations, and workflows to maximize value without compromising performance.
• 🤖 Humanity: Users value natural, human-like AI interactions. AI agents should deliver personalized communication, emotional intelligence, and memory-driven continuity.
• 🚀 Ready to Build Better AI Agents? Check out Voice Agent API, which combines speed, accuracy, and natural interactions to create AI agents that deliver real-world impact. Start here →

Imagine an AI agent in a hospital that alerts a doctor just in time to prevent a cardiac arrest, saving a life. Now, picture that same AI just a few moments too late, and a disaster unfolds. This thin margin between success and failure defines the stakes of building AI agents.

AI agents are becoming applicable to healthcare, customer service, and retail sectors, improving operations and enhancing user engagement. Building AI agents that deliver advanced services in these sectors requires navigating a delicate balance of technical and human factors. 

However, these systems can be posed with challenges, such as: 

• Low latency is vital to prevent user frustration and maintain engagement
• High accuracy is critical in sectors like healthcare, where mistakes can have serious consequences. 
• Balancing costs and quality poses another challenge: financial constraints can limit scalability and impact performance. 
• Finally, creating AI agents with human-like interactions is essential for fostering user trust, satisfaction, and adoption. 

To overcome these challenges, you must focus on four key pillars: latency, accuracy, cost, and humanity. 

This article discusses these in more detail and gives useful advice for developing AI agents that can handle the complicated needs of modern uses.

This section explores the four critical factors when developing and deploying AI agents for real-world applications.

We will assess each factor and discuss best practices to ensure their contribution to the operational success of these agentic systems.

In the context of AI agents, latency refers to the time difference between when an agentic system receives a command and when it responds to it.  

It is the invisible pulse of any AI interaction. In real-time applications, even seconds can differentiate between a seamless experience and a frustrating one.

Latency is critical across industries, directly impacting user satisfaction, trust, and operational success:

• In healthcare, telemedicine platforms rely on real-time data; delays over 250 milliseconds in remote monitoring can hinder timely interventions that risk patient outcomes. 
• In customer service, fast chatbot responses build trust, while live chat expects replies within 1-5 minutes.

Minimizing latency in AI agents is difficult for many reasons. These systems have many parts that work together, such as vector databases, embedding models, external tools, and the main large language models (LLMs).

To reduce latency, you must examine how well the LLM works alone and how well the broader agent architecture works.

Latency benchmarks provide critical insights into the responsiveness of LLMs in real-world scenarios. In the table below, you will see the key metrics for assessing LLM latency.

Benchmarking studies like this one from Chitty-Venkata K. et al. (2024) show that latency varies widely between LLMs, with smaller models usually having lower latencies than bigger ones.

Latency isn't just affected by the size of the model, though. Inefficiencies in the LLM agent workflow, such as using too many components or calling tools too often, can also slow down response times.

Some factors to minimize latency include:

🛠️ Optimize Core LLM Performance

Choose models appropriately sized for the task, as smaller models like Mistral 7B generally reduce latency in lightweight or time-sensitive applications. 

To reduce processing time, minimize input and output token lengths. Efficient prompt engineering can streamline input length without sacrificing task performance.

🔄 Streamline Workflow Orchestration:

A clear definition of the agent’s scope and tasks, as well as correct and optimized tool construction, can prevent redundant or inefficient workflows. 

Use parallel processing for independent tasks while ensuring sequential execution for dependencies or delegation when appropriate. Implement robust error-handling mechanisms to mitigate delays from failed tool usage.

⚙️ Enhance External System Efficiency

Optimize queries and embeddings for speed and relevance. Slow database operations or inefficient embeddings can bottleneck the entire agent workflow. Reduce the latency of external tools through API optimizations and preloading where feasible. 

For instance, caching frequently accessed data or results can minimize repeated calls. To reduce network transmission delays, deploy high-performance GPUs or TPUs optimized for LLM workloads and leverage edge computing for latency-sensitive tasks.

📈 Monitor and Iterate Continuously

Use open-source tools like LangTrace and AgentOps or proprietary tools to track latency and identify bottlenecks. Regularly analyze execution logs to refine workflows and ensure consistent performance improvements.

A key factor in latency is the orchestration of LLM agents, which depend on interconnected tools, workflows, and design complexities. Developers can significantly reduce latency and improve overall efficiency by optimizing LLM performance and improving system integration.

Accuracy in AI agents involves correctly interpreting user inputs and responding with precise, contextually relevant actions. It isn’t just about understanding the words in a query—it’s about grasping intent, contextual nuances, and specialized terminology. 

Accuracy is the foundation of trust and reliability, particularly in critical domains like healthcare, finance, and customer service, where errors can have significant consequences.

Here is what it involves:

1. Query interpretation: The ability to clearly understand user intent, even in cases where queries are ambiguous or expressed with diverse linguistic styles.
2. Contextual comprehension: Recognizing and applying the context surrounding a query to provide relevant, actionable responses.
3. Domain-specific knowledge: Accurately processing specialized terminology, such as medical terms, financial jargon, or industry-specific phrases.

For instance, in healthcare, confusing "Ativan" with "Advil" could lead to dangerous mistakes in patient care. Consistent delivery of accurate, grounded, and relevant responses determines the success of an AI agent. 

The real-world consequences of low accuracy in healthcare are stark, underscoring the need for robust and reliable systems. Specialized models like Deepgram’s Nova-2 Medical Model and DeepScribe Medical Scribe offer high transcription accuracy and contextual understanding tailored to medical settings.

Achieving high accuracy in LLM agents requires a multi-pronged approach.

🎓 #1. Fine-Tuning for Domain Specificity

Fine-tuning LLMs on domain-specific data improves accuracy, especially in nuanced fields like healthcare, where precise terminology is critical. 

But using newer, bigger, state-of-the-art models and LLM leaderboards like those on HuggingFace and Imsys to make decisions can improve accuracy and be especially helpful at lowering the number of hallucinations. 

This is because the latest versions of the models from model providers tend to better understand complex language, recent training data, and complex contexts. 

However, raw size isn't always the solution. Fine-tuned models, tailored to specific domains or tasks, offer a powerful alternative. For example, fine-tuning healthcare-specific models ensures an accurate interpretation of medical terminology.

📊 #2. Accuracy-Focused Evaluation

Ensure alignment with user intent and provide contextually relevant actions through key metrics:

• Query translation accuracy: Evaluate the effectiveness of converting user queries into actionable tasks.
• Tool appropriateness: Validate that the agent selects tools suitable for the query’s requirements.
• Grounded responses: Guarantee outputs are relevant, accurate, and traceable to real-world data to minimize errors and hallucinations.

🛡️ #3. Confidence Scoring and Human Oversight

Use confidence thresholds to flag uncertain responses for human review, particularly in high-stakes applications like healthcare or finance. This layered approach balances automation with reliability.

🔁 #4. Continuous Learning and Feedback

Implement feedback loops using user assessments to refine performance, considering techniques like reinforcement learning from human feedback (RLHF). 

Regular updates and fine-tuning based on evolving user needs and language trends ensure sustained accuracy.

⚙️#5. Workflow Orchestration and Validation

An LLM agent’s performance is only as effective as the systems supporting it. Optimizing workflows—whether through parallel task processing or error recovery mechanisms—prevents inefficiencies and inaccuracies. 

Moreover, validating each step of the agent’s actions against expected outcomes ensures outputs remain grounded and accurate.

Developing AI agents demands a careful balance of cost and quality across components like models, vector databases, APIs, and integrations. Proprietary options such as OpenAI's GPT-4o and Google's Gemini 1.5 Pro deliver top-tier performance but are costly, which makes them challenging for budget-sensitive projects. 

Meanwhile, open-source alternatives like Llama 3, those on Hugging Face models, or Groq Inference Engine offer affordability but may compromise latency, accuracy, or complexity. 

For instance, a high-volume customer service AI might benefit from GPT-4o's quality but find it unsustainable for scaling. In contrast, open-source models can save costs at the risk rate limits or inaccuracies that could lead to lower satisfaction. 

Achieving scalability and effectiveness requires strategic trade-offs tailored to budget and long-term goals.

To ensure cost-efficiency while maintaining performance, consider adopting the strategies below.

🔍 #1. Optimize Models for Efficiency

• Token management: Analyze input and output token lengths to minimize processing time and costs. Reducing unnecessary tokens through better prompt engineering can significantly decrease expenses.
• Cost-benefit analysis of moving parts: Review the cost implications of various components—such as APIs, vector databases, and external integrations—and choose open-source or cost-effective alternatives where appropriate. However, consider the trade-offs in privacy, reliability, and scalability before making decisions.
• Prompt tuning and versioning: Maintain detailed versions of prompts to track improvements and tailor them to specific use cases. You can also implement prompt caching, which stores users' prompts and responses so the system can reuse cached results for repeated or similar queries. This minimizes redundant computation, accelerates response times, and lowers costs.
• Cost-effective fine-tuning: If fine-tuning is necessary, use Parameter-Efficient Fine-Tuning (PEFT) to minimize computational and memory requirements. Starting with a smaller pre-trained model can also simplify the fine-tuning process, reducing costs while maintaining high accuracy.

🌐 #2. Use Open-Source Tools Judiciously

Platforms like Hugging Face provide access to pretrained and fine-tuned open-source models for various applications. By using these resources, you can reduce licensing fees. 

However, conduct a thorough cost-benefit analysis to address concerns about data privacy, rate limits, and ongoing support.

📊 #3. Monitor and Optimize Continuously

Implement monitoring tools like TruLens and WhyLabs to track system performance and resource usage. Ensure optimal resource allocation by reassessing workflows, computational demands, and integration efficiency. Observing these processes is critical to preventing unnecessary expenditures while maintaining performance.

Cost efficiency in AI development isn’t just about saving money—it’s about enabling scalability and delivering a superior user experience. Poor resource allocation can lead to budget overruns and stifle growth, while excessive cost-cutting risks compromising the product’s quality. 

By strategically balancing investment and scalability, you can create AI agents that are both high-performing and cost-effective. Efficient design and planning free up resources to expand features, enhance user experience, and scale operations. 

As AI adoption grows, mastering cost management is essential for staying competitive in an increasingly dynamic landscape.

Often overlooked, humanity plays a crucial role in developing efficient and reliable AI agents. Beyond speed and accuracy, creating natural, empathetic, and engaging interactions is key to earning user trust, satisfaction, and long-term adoption.

No matter how advanced, a robotic or impersonal agent risks alienating users. Human-like interactions aren’t optional but essential for fostering emotional connections and trust.

Studies done by Hernandez J et al. (2023) show users respond more positively to AI systems that understand emotions, adapt to needs, and engage intuitively. For instance, empathetic AI agents in healthcare can improve treatment adherence by offering personalized encouragement and understanding.

Traditional AI systems, like Interactive Voice Response (IVR) platforms, often struggle to create engaging experiences. Their rigid, pre-programmed responses fail to account for the nuances of human language, leading to frustration.

The common issues include:

• Lack of emotional awareness: Robotic systems often fail to recognize user emotions or respond appropriately.
• Repetitive and generic responses: Without the ability to adapt contextually, traditional AI interactions can feel monotonous and unhelpful.
• Difficulty understanding complex queries: Limited natural language capabilities can result in misinterpretations, leaving users feeling unheard.

These limitations highlight the need for AI agents that can bridge the gap between functionality and emotional intelligence.

Humanity in AI agents can be achieved through a combination of advanced techniques, thoughtful design, and continuous feedback:

🗨️ Incorporate Natural Language Understanding (NLU)

To deliver meaningful and effective interactions in specialized domains, it is essential to fine-tune large LLMs to improve their natural language understanding (NLU) capabilities. Fine-tuning allows these models to interpret context more accurately, recognize subtle linguistic cues, and deliver more natural and contextually appropriate interactions. 

For example, fine-tuned LLMs trained on diverse datasets can effectively manage slang, idioms, and regional language variations, resulting in personalized and human-like communication. This approach ensures users feel understood, even in complex or nuanced conversations.

🧠 Develop Emotional Intelligence

Emotional intelligence allows AI agents to recognize and adapt to user emotions to improve interaction quality. 

Analyzing sentiment through tone, word choice, or even emojis, the agent can respond empathetically or enthusiastically, depending on the context. AI systems can use Emotional Chain-of-Thought (ECoT) to align responses with emotional context.

🔄 Use Human-in-the-Loop Feedback

Training and testing AI agents with human feedback helps refine their interactions. AI systems can better mimic human conversational styles and improve their responses by analyzing real conversations and incorporating insights from human reviewers.

🔍 Capture Short- and Long-Term Memory

AI agents with memory capabilities can learn from user interactions within a session (short-term memory) and across sessions (long-term memory). 

This allows them to recall preferences, previous queries, or context from past interactions, creating a more seamless and personalized user experience. 

⚙️ Configure Parameters for Natural Behavior

Adjusting model parameters like temperature can make AI agents more human-like. A lower temperature value results in deterministic, concise responses, while a higher value can add creativity and variability, making conversations feel more organic.

Hume.ai specializes in developing AI models that are empathetic and capable of detecting users' emotions, allowing them to respond appropriately. One of their models, Empathetic Voice Interface (EVI), which you can access through an API, uses the user's voice tone to determine the appropriate reply timing.

Deepgram's Nova-2 model is at the heart of EVI's transcription abilities. Their documentation provides more information on integrating Deepgram's technology's speed with Hume's EVI's expressiveness.

Humanity in AI agents goes beyond mimicking behavior—it’s about forging meaningful connections that elevate the user experience.  

You can make AI systems that connect with users by emphasizing empathy, personalization, and ongoing learning. This will build trust, engagement, and satisfaction.

Creating effective AI agents hinges on four critical factors: latency, accuracy, costs, and humanity. Each element is crucial for ensuring performance, reliability, and user engagement.

• Latency ensures real-time responsiveness, delivering seamless interactions in time-sensitive healthcare and customer service applications.
• Giving accurate, situation-specific answers builds trust and dependability, which is very important in high-stakes fields like medicine and finance.
• Costs require careful balancing to ensure scalability and sustainable development without compromising quality.
• Integrating empathy, adaptability, and personalization into interactions makes them more human, which builds trust and encourages long-term use.

Addressing these pillars allows developers to create AI agents that meet technical demands while resonating deeply with users.

Build smarter, faster, and more human-like AI agents with Deepgram’s Voice Agent API.

And the easiest way to try out our Voice Agent API is in API Playground where you can quickly explore its capabilities without having to write any code.

Latency is the time it takes for an AI agent to process a user’s input and deliver a response. It’s critical for maintaining user engagement and ensuring timely actions, especially in real-time applications like healthcare or customer service.

High accuracy can be achieved by fine-tuning models on domain-specific data, using evaluation metrics like query translation accuracy, and implementing confidence scoring with human oversight. Continuous learning and workflow validation also help maintain precision.

Yes, leveraging open-source models, optimizing input/output tokens, and employing techniques like Parameter-Efficient Fine-Tuning (PEFT) can reduce costs. Careful budget planning and using scalable cloud solutions also ensure cost efficiency without sacrificing performance.

Incorporate Natural Language Understanding (NLU), emotional intelligence, and memory capabilities to make interactions more empathetic and personalized. Adjusting parameters like temperature can add conversational variability, enhancing the agent’s human-like behavior.

AI agents are transforming industries such as:

• Healthcare: Diagnostics, patient care, and remote monitoring.
• Customer Service: 24/7 support and query resolution.
• Retail: Personalized shopping experiences and inventory management.
• These industries rely on AI agents for efficiency, scalability, and user engagement.