How to Build an AI Agent

Collecting data: Listen to the caller

Before the AI agent can respond, it needs to collect relevant information.

In this case, automatic speech recognition (ASR) technology accurately transcribes voice inputs into text in real time and ensures the AI agent gets structured, usable data. It might also pull past interactions or customer relationship management (CRM) data to personalize responses.

So when a customer calls to check their order status, the AI agent identifies the caller using their phone number and retrieves their order details from the CRM database.

Reasoning: Figure out what the caller wants

Now that the AI agent has the data, it understands what the customer is asking. Using natural language processing (NLP) and decision-making models, it deciphers the intent and chooses the best response.

If the caller asks, “Where’s my order?” the AI agent quickly analyzes their request and retrieves the latest tracking details, providing an instant update without needing a human agent.

Action: Respond to the query

After understanding the intent, the AI agent takes action based on a predefined AI agent workflow. This could involve pulling information from a system, updating records, or escalating to a human agent.

For instance, if an order is delayed, the AI agent automatically notifies the customer and provides an estimated delivery time. If the customer wants to cancel, it can even process the request.

Learning: Improve responses over time

Here’s when deep learning for AI agents comes into the picture. A well-trained AI agent gets better with each interaction by learning from previous conversations and customer feedback. They can use call logs and machine learning models to refine responses.

For example, if many customers ask, “Why is my order late?” and tend to request human support afterward, the AI can learn to proactively offer solutions before escalating the call.

Communication interface: Learn from previous interactions

A great AI agent is also accessible on every platform so that businesses can ensure real-time conversations across multiple communication channels.

If a customer calls about their order status, the voice agent answers over voice. But if they later send a WhatsApp or SMS inquiry, the AI will remember the conversation history and continue to offer support without asking customers for details again.

Memory and profiling: Personalize customer experiences

When an AI agent remembers past interactions and adapts to user preferences, it automatically becomes more powerful. For example, Plivo’s AI-powered voice agents can store caller history, making future conversations smoother.

Let’s suppose the same customer calls about their order again. The AI agent recognizes them and starts with: “Hi [Name], I see you called earlier about your order. Do you need more details on the delivery timeline?”

Now that we know the building blocks, let’s understand how to build an AI agent.

Step #1: Define your business goal and purpose of the AI agent

To build an effective AI agent, define its purpose and business goal.

Are you looking for:

• A customer support AI assistant that answers FAQs?
• A fully autonomous agent that operates without human input?
• A marketing tool to analyze trends and offer insights?
• A virtual shopping assistant to recommend products and help close sales?
• An AI financial advisor for personalized recommendations?

For example, if you run an e-commerce store, a virtual shopping assistant such as Plivo's AI-powered voice bot can guide customers, recommend products based on their browsing history, and even help close sales, all without human intervention.

Customers can get real-time assistance while businesses increase engagement and conversions.

It's also important to consider the specific use cases and industry constraints. For example, a small clinic with only a few daily appointments may not require an AI agent, while a mid-sized hospital with high call volumes can benefit from one to manage patient scheduling.

Understanding your domain and challenges will help you build an AI agent that truly adds value to your business.

Step #2: Collect data to train the agent

Training autonomous AI systems requires high-quality data so they learn and improve their performance. Depending on their purpose, this data could include text, images, audio, call logs, transcripts, and more.

For example:

• A chatbot requires a vast dataset of conversations to understand human communication patterns.
• A recommendation engine analyzes user behavior data to make personalized suggestions.
• An AI voice agent needs call logs and transcripts to process speech patterns, detect intent, and improve response accuracy.

Once you have the data, it should be prepared for training. This includes fixing typos in text transcripts, filtering out background noise in voice recordings, etc. Plivo goes the extra mile as its profanity filters detect and mask inappropriate content in transcriptions.

Step #3: Choose the right machine learning model

The development of AI agents relies heavily on selecting the right machine learning (ML) model based on task complexity. Common ones include rule-based models, supervised learning models, and deep learning architectures like neural networks.

Choose models as per their respective use cases:

• A rule-based model works well for simple tasks like FAQ bots.
• A supervised learning model is ideal for AI agents who need to classify data or predict outcomes based on labeled datasets.
• A deep learning model is best for complex tasks like NLP and speech recognition.

You can also pick pre-trained models like a Generative Pre-trained Transformer (GPT) for AI agent development. They could be a great starting point as they've already been trained in human interactions.

Step #4: Train the AI agent

Training autonomous AI systems is where the agents learn from prepared data to perform their intended tasks.

Here are the key steps involved:

• Set up the training environment: Establish the necessary software libraries and the best frameworks for AI agents. For instance, you can integrate Plivo with Deepgram, OpenAI, and ElevenLabs to empower context-aware customer conversations.
• Split data: Import the cleaned and labeled data, then divide it into training and testing sets. The training set teaches the model, while the testing set evaluates its learning.
• Model training: Use the training data to teach the model, adjusting parameters to minimize errors and improve accuracy.
• Decide the parameters: Set values for batch size, learning rate, and other factors that influence how the model learns and adapts.

Step #5: Test and validate the AI agent

Before deployment, you need to ensure the AI agent functions correctly and meets performance standards. You can choose from the following testing methods:

• Unit testing: Evaluate individual components of the AI agent to ensure each part functions as intended.
• User testing: Have real users interact with the AI agent to gather feedback on its performance and user experience.
• A/B testing: Experiment with different versions of the AI agent to determine which performs better in terms of user satisfaction and task completion.

Additionally, consider setting up mechanisms to collect user feedback, such as surveys, feedback forms, or direct interviews. Use the feedback to continuously improve the AI agent.

If the AI agent doesn’t perform as per your expectations, revisit the training phase.

Step #6: Deploy and monitor the AI agent

Once the AI agent is trained, the next step is to deploy it and ensure it performs effectively. This involves:

• Deployment: Integrate the AI agent with the intended platforms, such as websites, apps, or customer service channels.
• Performance monitoring: Track key performance indicators (KPIs) like response accuracy, user engagement, and error rates to identify areas for improvement. If a voice agent frequently escalates calls, you may need to refine its intent recognition.
• Continuous improvement: Use real-time data and user feedback to retrain and fine-tune the AI agent so it adapts to evolving user needs and consistently delivers high performance.

You can understand AI tools better through real-world use cases. Let’s go through a few to fulfill specific goals.

Streamline routine financial operations

With real-time transactions reaching $5.3 trillion globally, the demand for instantaneous financial solutions is higher than ever. AI agents help businesses meet this demand.

Depending on your existing business gaps, decide whether you need an AI agent that analyzes large datasets and helps with stock analysis or a bot that provides instant support.

For instance, Plivo’s AI-powered voice bot simplifies financial services by providing instant account updates, processing transactions, and offering personalized financial advice, anytime, anywhere.

Optimize inventory management

Traditional inventory tracking methods often fail to provide real-time insights, causing stockouts that frustrate customers or surplus stock that ties up capital and increases storage costs.

Walmart has effectively used AI agents to optimize stock levels, reduce waste, and improve customer satisfaction by preventing understocking.

Improve patient communication in healthcare

AI agents streamline healthcare operations by automating appointment scheduling, sending reminders to reduce no-shows, and managing patient inquiries 24/7. They can also assist with prescription refills, route urgent cases to human staff, and provide multilingual support for better patient communication.

This helps healthcare providers improve patient engagement and scheduling efficiency, freeing up staff to focus on critical care.

Offer 24/7 customer support

Businesses aim to provide 24/7 assistance to meet growing consumer expectations. AI voice agents can facilitate this by handling inbound and outbound calls without human intervention, offering immediate responses, and resolving common inquiries.

Plivo, for example, significantly improves customer support operations by automating routine tasks, reducing wait times, and freeing human agents to address more complex issues.

Improve language learning with a virtual tutor

Over 16 million people in the U.S. speak English “less than very well.” Clearly, there is a substantial demand for effective language learning solutions.

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AI agents can provide personalized tutoring experiences by offering real-time translations and clarifying complex terms in learners' preferred languages. This technology supports inclusive learning environments, allowing students to overcome language barriers and engage more fully with educational content.