Mathverse recently launched an artificial intelligence (AI) agent that allows users to create unique cards and sell them through a blockchain-powered system. On the other hand, Shopify’s AI assistant, Sidekick, helps merchants analyze sales trends and automate tasks.

Clearly, AI agents are changing how businesses operate across industries.

A recent McKinsey report also shows that 78% of companies now use AI in at least one function, up from 72% earlier in 2024.

Despite learning about how AI agents benefit businesses, implementing them can feel like a steep and complicated gamble (not to mention a technical nightmare). You may be eager to improve your business's efficiency and still wonder: “How to build an AI agent that truly fulfills my business needs?”

In this blog post, we’ve addressed this question thoroughly so that you can build AI agents that cater to your needs.

What is an AI agent?

At its core, an AI agent is a smart software system that works on its own to complete tasks — whether that's answering FAQs, analyzing data, or handling transactions. It processes information, makes decisions, and helps businesses run smoothly.

However, not all AI agents work the same way. Some assist humans, while others take full control. Let’s break them down:

1. Assistive agents: These agents are like a co-pilot for your business tools. They help humans be more productive but don’t replace them. AI virtual assistants like Siri and Alexa are classic examples as they understand user queries and respond while keeping humans in the loop.
2. Autonomous agents: They operate without human intervention. Self-driving cars, warehouse robots, and AI agents in customer service that handle support without needing a human touch, all work on autonomous AI agents.
3. Hybrid agents: These agents assist when needed and take complete control when possible. For example, Shopify’s Sidekick suggests marketing strategies (assistive) but can also generate sales reports on its own (autonomous).

No matter the type of AI agent, they all rely on the same building blocks that make them function.

The building blocks of AI agents

An AI agent architecture consists of six building blocks. To see these building blocks in action, let’s walk through a real use case.

Use case: You want to build an AI-powered voice agent that handles tasks like answering FAQs, processing orders, or routing calls.

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.

Build and train AI agents in 6 steps

Building an AI agent may seem complex, but breaking it down into six clear steps makes the process straightforward. Let’s go through these steps in detail.

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.

Real-world use cases of AI agents

From finance and healthcare to inventory management, AI agents are transforming how businesses operate. Here are some business use cases to explore.

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.

Build and train your AI agent with Plivo

When a customer calls your support line for an order update, they expect a quick, natural-sounding response, just like talking to a real person. That’s exactly what you can build with Plivo’s AI-powered voice agents.

The moment a call comes in, Plivo’s AI agent transcribes the customer’s query using speech-to-text (STT). That message is then sent to ChatGPT (or a large language model (LLM) of your choice), which crafts a relevant response. Once the response is ready, Plivo converts it back into speech using text-to-speech (TTS) and plays it back to the caller.

No long wait times, no robotic scripts.

Whether you run an e-commerce store, a healthcare practice, or a financial service, Plivo lets you create an agent that suits your needs. And if you’re not ready to switch to voice, start by automating text-based communication for a smoother transition.

Ready to build AI agents without the hassle of coding or complex integrations? Contact us today!

ByteDance has unveiled a groundbreaking innovation that promises to redefine how we perceive and interact with video content. Enter Goku AI, an advanced video generation model that is set to transform advertising, media, and content creation by making AI-generated videos indistinguishable from real ones. Let's explore the fascinating capabilities of this new technology and its potential impact on various industries.

The Power of Goku AI

ByteDance's Goku AI stands out as a revolutionary tool in the realm of AI-driven video generation. At its core, Goku is a flow-based video generative foundation model capable of creating highly realistic videos. Goku truly shines with its ability to generate videos of people interacting with products using nothing more than text descriptions. Imagine typing a few sentences and watching as a lifelike video emerges, showcasing your product in action.

This capability is not just a novelty; it significantly streamlines the content creation process for brands. By converting still product images into dynamic videos, Goku AI simplifies the task of producing engaging visual content, thereby opening new avenues for creativity and efficiency.

Goku+: A Game Changer for Advertising

ByteDance has introduced Goku+, a specialized video ads foundation model, alongside the standard Goku model. Goku+ is designed to revolutionize the advertising industry by reducing costs by an astonishing 100 times compared to traditional advertising methods. This cost-effectiveness is achieved without compromising on quality, as Goku+ produces videos that are virtually indistinguishable from those featuring human actors.

The implications of this technology are profound. Brands can now create compelling video ads with minimal resources, allowing even small businesses to compete on a level playing field with larger corporations. The democratization of high-quality video content creation could lead to a more diverse and innovative advertising landscape.

Realism and Beyond: Animating the Impossible

Goku AI impressively animates characters with natural movements, facial expressions, and gestures. Sophisticated algorithms achieve this level of realism by mimicking the subtleties of human behavior. As a result, AI-generated characters appear lifelike and engaging, enhancing the viewer's experience.

The potential applications of this technology extend beyond advertising. In the entertainment industry, for instance, Goku AI could lead to movies and digital content that do not require human actors. This shift could inspire new forms of storytelling and creative expression, as filmmakers explore the possibilities of AI-generated narratives.

The Future of AI in Content Creation

As advancements from Goku AI suggest a world where AI plays a central role in content creation, we can focus more on innovation and storytelling due to the ability to produce realistic videos at a fraction of the cost and effort. The technology also raises important questions about the nature of creativity and the role of human input in the digital age.

ByteDance's Goku AI represents a significant leap forward in the field of AI-driven video generation. Its potential to transform advertising, media, and content creation is immense, offering new opportunities for efficiency and creativity. As we embrace this technological evolution, we must consider the broader implications and possibilities it presents.

What do you think the future holds for AI-generated content, and how might it reshape the way we tell stories and connect with audiences?

An AI chatbot in an e-commerce store can handle simple questions like, "What's your return policy?" But what happens when a customer says, "I got the wrong item and want to exchange it for a different one?" Since this request is more complicated, the bot doesn’t know what to do and directs the customer to a human agent. That means longer wait times and frustration.

On the other hand, agentic artificial intelligence (AI) understands what the customer wants, pulls up order details, checks if the item is in stock, and processes the exchange without a human stepping in. Agentic AI builds on the capabilities of generative AI tools like ChatGPT to actively make decisions and optimize workflows.

That’s why experts predict that by 2028, 33% of enterprise software will have agentic AI built-in.

In this guide, we’ll walk you through what is agentic AI, its benefits, and real-life use cases so you can discern whether it’s a good fit for your business use cases. 

Agentic AI vs. Gen AI

Agentic AI goes beyond simply responding to prompts. It actively perceives its environment, reasons through options, takes action, and learns from outputs.

In contrast, generative AI processes inputs, infers patterns, creates outputs, and adapts to new contexts. Traditional AI follows predefined rules.

Here's how they compare.

Now that we’ve covered how agentic AI differs from traditional and rule-based AI, let’s explore how it can benefit businesses.

Why agentic AI matters: Key benefits for businesses

AI is evolving, and its role in business is growing. Rather than just responding to commands, it can now analyze, adapt, and take action. This shift is transforming how businesses operate and compete.

Here’s how.

Increased workflow efficiency and productivity

Agentic AI handles complex, multi-step tasks, freeing employees to focus on higher-value work that requires human creativity and expertise.

For instance, ServiceNow's AI agents have reduced case resolution time for complex cases by 52%.

Similarly, when used in customer service, agentic AI can analyze a ticket, determine the root issue, draft a response, escalate complex cases, and even follow up with customers.

More strategic human-AI collaboration

Agentic AI enhances human decision-making through complex data analysis and actionable insights. It takes on tasks like risk assessment, fraud detection, and patient diagnostics, allowing professionals to concentrate on higher-level decisions.

In the travel industry, AI self-learning models manage bookings, optimize flight schedules, and handle customer queries in real-time. This reduces operational costs and frees up human resources for more personalized customer interactions.

Beyond automation, agentic AI also understands human intent and urgency.

For example, OpenAI’s Operator can handle entire tasks autonomously. So if a customer needs last-minute catering for an event, instead of just placing an order, an AI-like Operator could find a restaurant that meets dietary preferences, check availability, confirm the order, and even handle payment, creating a human-like customer experience.

Enhanced trustworthiness and specialization

Agentic AI systems analyze datasets to identify patterns, leading to more informed and trustworthy decisions. In the legal field, agentic AI can analyze legal documents, identify patterns in case law, and even assist in developing legal strategies.

The National Law Review says, "In the 20th century, mastering 'thinking like a lawyer' meant developing a rigorous, precedent-driven mindset. Today, we find ourselves on the cusp of yet another evolution in legal thinking — one driven by agentic AI models that can plan, deliberate, and solve problems in ways that rival and complement human expertise."

Let’s understand these benefits further through real-world use cases of agent-based AI models.

Use cases of Agentic AI

From improving customer experience to empowering financial decision-making, the use cases of agentic AI scatter across industries. Let’s take a closer look at how it’s making an impact.

Improve customer experience 

To qualify as agentic AI, a system must perceive, reason, act autonomously, and learn from its actions.

Say you integrate Plivo's AI-powered voice agent to handle frequently asked queries. It can serve as a 24/7 customer support system, manage contextual voice interactions, and trigger workflows automatically.

You can build on this by creating another decision-making bot with an engine like GPT-4, Google's Gemini, etc., that analyzes customer intent, and adapts responses.

Now, when a caller says, "Please refund or exchange the product!" the AI assesses the request. One system checks refund eligibility, while another evaluates whether an exchange is possible.

Businesses are still in the early stages of adopting agentic AI for customer service, but the shift is accelerating. By 2029, Gartner predicts AI will autonomously resolve 80% of common customer service issues, significantly reducing response times and improving customer experiences.

Create personalized content

Agentic AI helps businesses create personalized content by making sense of vast amounts of customer data.

Yum Brands, the parent company of Taco Bell, KFC, and Pizza Hut, used AI-driven marketing to send personalized emails and notifications to their customers. They analyzed what customers typically order, what they prefer, and how their choices change over time to send hyper-personalized offers at the right moment.

As a customer interacts with the brand, agentic AI adapts the marketing message based on their evolving needs and behaviors. This ensures every conversation made with the customer is relevant and hyper-personalized.

Beyond messaging, you can even automatically adjust variables like bidding, ad placement, or audience targeting to optimize campaigns and conduct A/B testing at scale on multiple variables.

Improve patient care

Agentic AI systems can process vast datasets such as clinical notes, patient histories, lab results, medical guidelines, and even diagnostic imaging to extract actionable insights.

The National Health Service (NHS) introduced an AI physiotherapist named Kirsty to help patients with back pain. This AI agent offers same-day virtual appointments, personalized exercise plans, and real-time health advice, reducing wait times and improving accessibility.

Here’s how agentic AI improves healthcare workflows:

• Data coordination: When new clinical data is entered into an Electronic Medical Record (EMR), an AI-powered system pulls information from multiple sources and triggers workflows based on predefined logic.
• Specialized AI agents:
  
  • A clinical data agent analyzes patient records using Natural Language Processing (NLP).
  • A molecular test agent interprets genomic data from biopsy samples.
  • An imaging analysis agent processes radiological scans and pathology reports.
• Coordinated decision-making: While specialized AI agents operate independently, a coordinating agent synthesizes their insights to recommend the most appropriate clinical decision.

Note: Although fully agentic AI systems are still evolving, healthcare providers are already leveraging AI-powered voice bots that automate patient interactions, appointment scheduling, and medication reminders. Solutions like Plivo can further personalize patient interactions and help reduce wait times.

Empower financial decision-making

The day when a trading AI agent analyzes market data, monitors market trends, adjusts strategies, and mitigates risks isn't far. Agentic AI will make this possible by integrating tools via application programming interface (APIs), sensors, and advanced reasoning.

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Agentic AI could also autonomously assess micro-loans for smallholder farmers, using local data to evaluate risk without direct human involvement. Similarly, mobile banking powered by agentic AI could offer personalized micro-insurance products based on real-time weather data.

Optimize logistics and supply chain

When running an e-commerce business, the last thing you want is a customer placing an order for a high-demand product that’s actually out of stock. That’s where agentic AI steps in.

SAP has introduced two AI agents to tackle this issue: one for sales and another for supply chain management.

The sales AI agent determines the best price and product bundle for the customer while simultaneously checking inventory. Before making a sales commitment, the supply chain AI agent steps in to verify stock levels, assess delivery timelines, and adjust logistics accordingly.

Since these AI agents interact autonomously, they prevent sales teams from overpromising on orders that the supply chain can’t fulfill.

SAP CEO Christian Klein emphasized that contextualizing data is key to making agentic AI successful. “While 80% of businesses may not yet have the infrastructure to support AI-driven operations, SAP is bridging that gap by integrating predictive AI and automation directly into its software.”

Given the advancements in agentic AI, it’s only natural to wonder what the future holds.

What lies ahead: The future of agentic AI

Agentic AI systems provide the best of both worlds: LLMs handle tasks that benefit from dynamic responses, and these AI capabilities with conventional rule-based programming. So, the future of agentic AI consists of systems that fetch real-time information, retrieve updates, or pull specific data points important for decision-making.

However, as businesses integrate AI deeper into their operations, regulatory frameworks struggle to keep pace. A recent survey found that 93% of professionals recognize the need for clearer AI regulations to mitigate risks.

Ethical concerns, such as algorithmic bias, decision transparency, and compliance with evolving privacy laws, remain critical challenges. Companies must ensure AI-driven decisions are fair, explainable, and aligned with regulatory standards.

In industries where customer interactions matter, such as finance, healthcare, and e-commerce, solutions like Plivo help businesses use AI-powered voice and messaging tools to improve customer experiences while maintaining compliance.

Take the first secure step to agentic AI with Plivo

As agentic AI continues to evolve, businesses need AI-powered solutions that can learn, adapt, and improve over time. Plivo’s AI-powered voice agents make this transition seamless.

If you’re already using AI agents, you can take this a step further by building your own AI agent combining these two functions. You can launch the voice agents with any text-to-speech (TTS), speech-to-text (STT), and language model of your choice with Plivo’s APIs.

Whether you're looking to deploy autonomous AI agents, use AI for complex problem-solving, or build an entire AI-driven ecosystem, Plivo makes it easy.

Contact us today to explore the possibilities!

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We’ve all experienced single AI models working independently, whether it's a bot answering questions or an algorithm making recommendations. But what happens when a single AI system isn't enough for a business?

When your call center deals with complex, multi-step customer queries, or you need a broader perspective on stock marketing trading and risk management, single AI agents can fall short. It’s no surprise that AI agent applications in customer service and virtual assistants are expected to account for approximately 78.65% of the market share by 2035.

If you’re considering multi-agent AI systems for your business, it’s only natural to wonder: what is a multi-agent system? What are some multi-agent system examples, and applications? We’ll cover all this and more in this blog post.

Let’s get started.

What is a multi-agent system?

A multi-agent system (MAS) is a network of single AI agents that work together to achieve business goals.

For instance, if your online store deploys a single AI chatbot, it can handle answering FAQs, giving order status updates, etc. But if a customer raises multiple queries to track an order and request a refund, a single AI agent struggles to juggle both.

In this case, introducing multiple AI agents to assist customers with refund requests, return policies, sentiment analysis, and more, reduces response time and increases customer satisfaction. In fact, 57.7% of AI-driven customer support agents now operate using a MAS to handle complex interactions. 

It operates on three core principles:

1. Autonomy

Each agent in a MAS operates independently, without waiting for a response or instruction from a central system. For instance, in an AI-driven customer support system, a refund policy handling agent retrieves the policies without waiting for instructions from another agent.

2. Collaboration

While agents in an MAS work independently, all these distributed AI systems communicate and coordinate to solve problems. For queries like, "Where's my order, I want to cancel it." The ‘order tracking’ agent and the ‘cancel orders’ agent collaborate to provide a response that addresses both queries.

3. Distributed control

Multi-AI agents distribute tasks that self-organize based on real-time inputs. For example, Google Maps doesn’t have a central system dictating traffic patterns, road closures, and delays.

Now that we’ve covered what MAS is, let’s break down how it operates in real-world scenarios.

How multi-agent systems work

Multi-agent systems work as a well-coordinated team, where each agent has a role, and follows certain rules. Here's a quick breakdown of the key components:

• Agents: These are independent decision-makers. In customer support, one might handle FAQs, another may tackle refund requests, and so on.
• Environment: This is a space where agents operate, like a website, within transaction data, or customer interactions.
• Perception and data collection: This is when agents sense surroundings (customer tone, transaction data).
• Communication protocols: Agents need structured ways to talk to each other. For example, how drivers (agents) communicate in ride-sharing apps or when riders request a ride.
• Decision-making mechanisms: Each agent must then decide how to act based on the situation. For instance, sending a cancellation confirmation or rerouting calls.
• Coordination strategies: Agents must collaborate efficiently, just as robots coordinate to pick and ship items without colliding.
• Learning and adaptation: Smart agents learn from experience. If multiple customers have order or refund-related inquiries, AI-driven agent-based modeling analyzes these patterns to help you make informed decisions.

Control architecture: Multi-agent systems can be centralized, where a single entity directs all agents (like an air traffic control tower managing flights), or decentralized, where agents operate independently while coordinating with each other (like multiple drones adjusting their paths in real-time).

Real-world applications of multi-agent systems

Multi-agent systems are working behind the scenes in many industries. They help manage traffic, improve customer support, and even assist in public health efforts.

Here’s how they make a difference in the real world.

Manage transportation systems

Traffic congestion during peak hours or roadblocks during accidents can bring an entire city to a standstill. Multi-AI agents tackle this with real-time voice updates and intelligent coordination.

In Pittsburgh, the deployment of the Scalable Urban Traffic Control (SURTRAC) system led to a 25% reduction in travel times and a 40% decrease in vehicle wait times at intersections. They used multi-AI agents that communicate to adapt traffic signals based on real-time conditions, effectively easing congestion and improving traffic flow.

So if a major highway causes a traffic jam, one AI agent can instantly analyze traffic while another delivers voice updates via navigation apps, guiding routers to take alternative routes.

Prediction and prevention in healthcare and public health

Missed appointments and no-shows in the U.S. healthcare system are leading causes of healthcare inefficiency. Even charging full fees for no-shows or missed appointments will cost you patient frustration and retention issues.

MAS bridges this gap by improving patient engagement and ensuring timely care.

For instance, Plivo's smart healthcare interactive voice response (IVR) system helps providers automate patient communication and optimize call handling.

When a patient calls a clinic, Plivo’s smart IVR can screen symptoms using AI-driven voice interactions and direct them to the appropriate specialist based on urgency. Instead of long wait times or misrouted calls, the system efficiently prioritizes critical cases while handling routine inquiries without human intervention.

Automate customer support

93% of customers are more likely to stay loyal to businesses with great service but achieving that at scale can be a challenge. In fact, long wait times and misrouted calls frustrate customers and overwhelm support teams.

AI-powered multi-voice agents improve customer service efficiency since businesses automate routine queries, ensuring customers get instant responses without waiting on hold. Smart call routing directs calls to the right department, reducing frustration, while speech recognition and customer relationship management (CRM) integration can send personalized follow-ups — whether it’s confirming appointments or providing order updates.

Streamline routine financial tasks

Managing routine financial tasks shouldn't feel like a constant back-and-forth. Multi-AI systems work together to streamline everything from payment confirmations to loan processing.

For instance, if you use one of Plivo's AI-powered voice agents to automate reminders, others can take care of fraud alerts, and give account updates in real-time. Depending on your customer base and their queries, you can customize these AI agents to provide contextual responses.

Connect components of supply chain management

Supply chain inefficiencies cost businesses an average of $1.3 trillion annually, representing nearly 10% of the global gross domestic product (GDP).

The power of MAS is that it makes every part of the supply chain, from warehouses to delivery trucks, streamlined. You can use one agent for supply management, another for inventory management, and one for demand forecasting.

It’s also convenient to send voice notifications for order confirmation, delays, and even emergency alerts in case of weather delays, stock shortages, etc.

Benefits of multi-agent systems

Multi-agent systems bring a lot to the table, making complex processes more efficient and intelligent. Let’s take a look at what makes them so useful.

Flexible

AI is already making 81% of companies respond faster to market shifts. MAS adds to this benefit as it adapts to changing business needs and automates processes like logistics, customer support, and compliance.

Scalable

Multi-agent systems are inherently scalable. As the complexity or scope of a problem increases, additional agents can be introduced to handle new tasks or responsibilities. This scalability makes these systems suitable for an array of applications, and dynamic environments.

Businesses can handle fluctuating demand without adding human resources, which reduces operational costs.

Robust

Multi-agent systems improve fault tolerance so if one AI component fails, another takes over. This ensures continuity, especially critical for industries like healthcare and finance.

Efficient

One of the primary benefits of multi-agent systems is the separate modeling-based AI agents. As each agent focuses on a singular task, agents can perform more efficiently and reduce manual errors.

Make multi-agent systems smarter with Plivo’s AI-powered voice agents

Autonomous AI agents often hit a dead-end as businesses scale, frustrating customers with repetitive, or worse, generic responses. Plivo's AI-powered voice agents understand your existing business gaps and fill those with adaptable solutions across industries.

These agents assess customer intent in real time so that callers don't waste time navigating endless IVR menus. Moreover, they sense customer sentiment, redirect them to the right department, and help gauge honest feedback to make informed decisions across tech support, appointment scheduling, or billing.

Plivo also offers data-driven analytics, where you can gain business insights and optimize customer interactions. Whether you're in healthcare, finance, or retail, Plivo's AI-powered voice agents ensure every conversation is efficient, personalized, and frustration-free.

You can use AI agents to speak your brand's language, and personality, get a 360-degree customer snapshot to make context-aware conversations, or resolve queries by utilizing internal business documents like FAQs, SOPs, and blog posts.

Contact us to deploy AI voice agents using Plivo, convert leads, resolve queries instantly, and send timely promotions in over 220 countries and territories.

A customer calls with a question. Before they even finish asking, one artificial intelligence (AI) agent is already listening, another is digging through past chats, and a third is crafting the perfect response.

It’s similar to having a team of experts working behind the scenes — fast, efficient, and always on point.

That’s the power of multi-agent artificial intelligence.

In 2025, AI isn’t a lone worker anymore. Companies like Google DeepMind are pushing it further with projects like Scalable Instructable Multiworld Agent (SIMA), where AI agents team up to follow human instructions in 3D virtual worlds. They’re training these agents to explore, build, and solve problems in video games, adapting to new tasks as a group.

When these AI agents work together, they handle challenges faster and better than a single agent could. Curious how this is changing things? Keep reading to find out!

How AI agents team up

While a single AI agent can be helpful, the real power of AI emerges when multiple agents work together. These systems bring specialized agents with unique skills to tackle complex or large-scale tasks that would be difficult for one agent to handle alone. This teamwork makes it easier for organizations to automate and improve their processes.

Here’s how multi-agent AI systems work:

• Understanding requests: One or more agents process the input, breaking it down to determine intent and key details.
• Planning workflows: Another set of agents maps out the necessary steps, assigning tasks to the right agents.
• Coordinating the team: A dedicated agent ensures that all AI agents communicate and work in sync.
• Executing tasks: Specialized agents handle their assigned steps, whether retrieving data, generating responses, or performing calculations.
• Collaborating with humans: If human input is needed, an agent flags the task and integrates their feedback.
• Validating outputs: Before delivering a final response, agents check for accuracy, consistency, and relevance.

These systems often combine standard agents like those handling user requests or managing data with specialized agents that have unique tools or skills, such as pulling data or interpreting images. Together, they work toward a goal you set.

At the heart of every agent is a large language model (LLM). This helps them understand what you’re saying and the situation around it.

Depending on the task, all agents might use the same model, or each could use a different one. This setup lets some agents share what they know while others double-check the work, making everything more reliable and consistent.

The system gets even better with shared memory. It stores information for the short- and long-term. This cuts down on how often humans need to step in during planning, checking, or refining a project.

Here’s the process in action:

• The system takes a complicated task and breaks it into smaller, more manageable parts.
• It assigns each task to the agent best equipped to handle it.
• Agents and humans collaborate seamlessly throughout the process.

Where you’ll see AI agents in action

AI agents use machine learning (ML) and advanced algorithms to make decisions, interact with diverse environments, and adapt to changing conditions. These systems are changing industries by making work faster, more accurate, and tailored to people’s needs.

Here’s how AI agents are helping out in different areas, with real examples of them in action.

Customer service

Businesses often deal with lots of customer questions and need to help people who speak different languages. This can get tough and expensive if not handled well. AI voice agents step in to make things easier by taking care of basic conversations in a way that feels natural.

Plivo’s AI voice agent, for example, can talk to customers in real time, picking up on their accents and feelings.

It works in 27 languages, which is great for companies with global customers. The voice agent also cuts costs by up to 40% and offers an uptime of 99.99%, so businesses can use it for everyday questions while human agents handle the harder ones.

Healthcare

Doctors and nurses have a lot to do, like seeing patients, filling out forms, and checking on health changes. This can eat up time they’d rather spend with people. AI agents lighten the load by handling some of these responsibilities.

Picture a doctor’s office where the physician is swamped with patient visits and notes to write up.

Oracle Health’s Clinical AI Agent fits right into this scene.

It listens to what patients say during appointments, writes up the records automatically, and even responds to voice commands. This cuts down on paperwork time, letting the doctor spend more timewith patients.

Logistics

Delivering packages sounds simple. Just take them from one place to another. But traffic jams, bad weather, or last-minute changes can make it difficult.

Companies need to figure out the fastest, cheapest way to get orders to customers on time. AI agents help by looking at all these factors and picking the best plan for deliveries, whether it’s by truck or drone.

Think about how Amazon handles millions of online orders every day. Their AI steps in to optimize delivery routes, checking traffic updates in real time to dodge delays and save gas.

Another company, Dista, uses an AI agent to watch traffic and weather, helping drivers make deliveries on the first try.

Supply chain precision

Running a supply chain means figuring out what customers will buy, ordering just enough stock, and making sure shipments go smoothly. If you get it wrong, you might run out of stock or have too much sitting around.

AI agents team up to solve this by guessing what customers will buy, ordering the right amount, and fixing shipping hiccups.

Take Walmart’s inventory system as an example. Shelves need to stay stocked with everything from cereal to socks. The AI looks at old sales and trends to predict what people will want.

Then, another AI agent tweaks orders to match those guesses, while a third keeps an eye on shipments, rerouting them if there’s a delay.

This agent shares info instantly, so suppliers and stores stay in sync. Companies using AI like this have seen 15% savings in logistics, 35% less extra stock and 65% increase in service levels.

Employee support

HR teams spend a lot of time onboarding new hires, answering questions, and setting up training. It’s a lot to juggle, and it can slow things down. AI agents step in to handle these routine tasks, making life easier for employees and giving HR more time to focus on people management.

Companies like IBM and Microsoft are leading the way with AI-driven HR tools.

IBM’s Watson, for example, automates administrative tasks and personalizes onboarding, helping new employees feel supported and engaged from day one.

Challenges that AI agents will bring

AI agents are incredible tools, but they come with challenges like energy consumption, privacy and ethics, and the costs and complexity of building them.

Here’s a detailed examination of each.

Energy consumption

Generative AI models, which power many AI agents, use a massive amount of energy. When training massive models like GPT-3, they churn out greenhouse gases equivalent to what several cars would produce over many lifetimes.

Even a single chat with one of these models can use up to 10 times more electricity than a quick Google search.

Looking ahead, experts predict that AI could be using as much power as a small country like Ireland. That’s a lot to wrap your head around!

For businesses relying on AI agents, say, for writing customer replies or generating healthcare reports, this ramps up both their energy bills and their environmental footprint.

To tackle this, opt for smarter solutions like designing energy-efficient algorithms, using specialized AI chips, and switching data centers to renewable power sources.

Privacy and ethics

AI agents use huge amounts of data to do their jobs. But here’s where it gets tricky: when that data gets shared, privacy and ethical questions pop up fast.

Picture a customer service bot passing along your chat details or a healthcare agent dealing with your personal health stats. If that info isn’t handled carefully, it could end up in the wrong hands or be misused.

AI often makes decisions without explaining how it reached them. This lack of transparency can hide biases and lead to unfair outcomes.

Research from the Information Systems Audit and Control Association (ISACA), highlights how this lack of openness is a real problem.

So, who’s keeping an eye on these systems? That’s the big question.

The solution is strong oversight, clear regulations like the General Data Protection Regulation (EU), and greater transparency in AI. People have a right to know how their data is used, and AI systems should be able to explain their decisions.

That’s the key to keeping things fair and safe.

Costs and complexity

Building an AI agent takes careful planning, design, coding, testing, and finally, deployment. Each step requires skilled experts and a well-planned budget to bring it to life.

Scaling them comes with issues such as inconsistent data quality and rising costs, as McKinsey highlights.

The complexity comes from needing top-notch experts, massive computing power, and constant training. For example, Meta’s LLaMA 2 took millions of GPU hours to train, racking up millions in hardware costs alone.

But there’s hope: businesses can cut corners (in a good way!) by using pre-trained models, tapping into cloud services, or grabbing open-source tools. These tricks bring the price down and make the process less of a headache.

And as more companies bring in generative AI agents, costs will likely reduce. This could open the door to new customer experience (CX) options, like offering human support as a premium service for those who want a more personal touch.

Trends to watch

As we look ahead, AI agents are gearing up to play an even bigger role in our lives. These smart systems are evolving fast, and a few exciting trends are starting to take shape.

Here’s what’s next for AI agents and why it’s worth paying attention.

AI agents will be everywhere

AI agents are popping up everywhere, handling everything from customer service chats to complex business operations. Companies are using them to schedule meetings, analyze data, and even assist in decision-making, while everyday users rely on them for things like smart home control and personal assistance.

As AI keeps improving, these agents will become even smarter, more independent, and a natural part of how we work and live.

The AI agents market is expected to grow from $5.1 billion in 2024 to $47.1 billion by 2030. That indicates an annual growth rate of 44.8%.

AI is already changing industries:

• Cineplex slashed customer service time from 15 minutes to 30 seconds per request. It processed over 5,000 refunds in five months.
• Fujitsu raised productivity by 67% with AI-made sales proposals.

Enterprises should catch up by 2025. Looking forward, AI agents will do more:

• By 2028, 33% of enterprise software might include AI agents. That’s up from less than 1% now. They’ll handle 15% of daily work decisions like approving loans or writing code docs.
• Beyond work, they could power self-driving cars to reduce accidents or act as personal tutors to improve learning.

Some believe AI agents could develop emotional intelligence, sensing moods and responding with empathy. It’s an exciting idea, but getting there isn’t easy.

Integrating AI agents into businesses comes with challenges. Companies often struggle to fit them into existing workflows, which means training employees and adjusting business models. As AlphaNeural points out, adoption focuses on the tech and making it work in the real world.

Despite these hurdles, AI agents will reshape work, learning, and connections. Their rise demands careful handling to benefit users while honoring values. With the right steps, they’ll be partners not just tools in a future we can embrace.

Problems will be spotted before they happen

What if problems could be fixed before they even happen? Machines could get tuned up before breaking down, and customers could receive help before they have to ask.

Thanks to AI agents, smart systems that predict and prevent issues using data, businesses are adopting them fast: 42% of enterprises used AI in 2023, and over 80% might by 2026. Why? It saves time, cuts costs, and keeps them ahead.

Companies succeed by identifying key challenges, selecting the right technology, training their teams, and starting small before scaling up.

And it’s paying well off across industries:

• In hospitals, AI catches deadly sepsis before it’s too late. It scans health records and vital signs, predicting trouble hours ahead. A study showed it cut deaths by 39.5% and shortened stays by 32.3%.
• Meanwhile, in finance, AI fights fraud as check scams soar 385% since the pandemic. The U.S. Treasury’s AI recovered $375 million in 2023, then $4 billion in 2024.
• AI keeps factories running in manufacturing. It uses sensor data to predict machine failures. General Motors, with IBM Watson, cut downtime by 15% and saved $20 million yearly. Plus. it could beat traditional fixes by 8-12%, or even 40% for reactive plants.

There are hurdles, though. Data must be clean, or predictions fail. Ethics matter — who’s to blame if AI messes up? Security’s critical too, with all that data flowing. Still, the wins are big: early fixes save cash, and experts say AI could boost profits by $4.4 trillion yearly.

Looking forward, AI agents might run supply chains or make tough financial calls, but we’ll need rules for fairness and accountability.

Experiences will be tailored like never before

AI agents predict issues before they arise, making experiences smoother and more personalized. From shopping to healthcare, they’re already shaping how we interact with services.

For example, streaming services like Netflix and Amazon use AI to figure out what we might like based on what we’ve browsed or watched before. It makes suggestions that keep us hooked.

Health apps like Fitbit tap into data from wearables to give us custom health tips, while smart home systems like Google Nest tweak our lights and thermostats based on whether we’re home and what we prefer.

Looking forward, AI agents are set to get even sharper. They could soon pull together all kinds of data like how we’re feeling or where we are to guess what we’ll need next.  Imagine an AI noticing you’re stressed and offering a calming playlist. Or one that knows you’re near a store and pings you with a deal on something you’ve been eyeing. 

Some might even predict you’ll need hiking boots for that trip you’ve got planned, urging you to grab them early.

Building AI agents will be made easy

Thanks to advancements like deep learning and natural language processing (NLP), AI agents are getting sharper. But it’s not just for tech experts anymore.

Tools like LangChain and AutoGen provide easy-to-use interfaces, while no-code platforms like Bizway and Lyzr let anyone craft AI agents without coding.

Cloud computing also helps by offering accessible power and data. Surprisingly, this shift is empowering solo entrepreneurs and small businesses to spark fresh ideas in unexpected areas.

Start your AI-powered future with Plivo

In 2025, AI agents are transforming how businesses handle tasks, especially in customer support. With growing demand for quick, reliable assistance, teams can easily feel overwhelmed.

Plivo AI steps in as a smart solution, designed to ease the burden while keeping everything running smoothly and efficiently. It aligns perfectly with the future of AI — smarter, scalable, and built to adapt.

Here’s how Plivo AI empowers your support team:

• Always available: Provides 24/7 support to deliver fast answers to customers when needed.
• Scales effortlessly: Manages peak demand without missing a beat.
• Personalized touch: Draws on past interactions to craft responses tailored to each individual.
• Streamlines processes: Walks users through complex steps, reducing frustration.

Letting Plivo tackle the routine tasks helps your team zero in on what really counts. Even better? It’s cost-effective, with a free trial to get you started.

But don’t take our word for it! Here’s what one of our users has to say:

Think of Plivo AI as a dependable partner, ready to support you today and into the future. Contact us today to see it in action.

Passwords have been the default security tool for years. But here’s the problem: they’re not enough anymore.

In 2021, 85% of data breaches happened because of phishing or stolen passwords.

The lesson? If you’re only using passwords, you’re leaving the door wide open for attackers.

That’s why two-factor authentication (2FA) and multi-factor authentication (MFA) exist. These tools add extra layers of security. In fact, studies show 2FA stops 96% of bulk phishing attacks, whereas MFA is capable of blocking 99.9% of automated account breaches.

But not every business needs the same level of protection. A small online store won’t need the same setup as a hospital handling patient records.

This article breaks down how 2FA and MFA work, where they fit in 2025, and how to choose the right approach for your needs.

What are 2FA and MFA?

You’ve probably come across 2FA and MFA when setting up security for your accounts. They both add extra protection, but what exactly do they mean? Let’s break them down and see how they work.

2FA: Two steps, stronger security

2FA does exactly what the name says: it adds two steps to verify your identity. Here’s how it works:

1. Step 1: You enter your password (something you know).
2. Step 2: You provide a second proof, like a one-time code sent via SMS or email (something you have).

For example, when you log into your bank account, you might type your password and then get a text message with a 6-digit code. Without that code, hackers can’t access your account — even if they steal your password.

2FA isn’t bulletproof, but it’s a massive upgrade over passwords alone. It’s like adding a deadbolt to a door: not impossible to break, but way harder for intruders.

MFA: More layers, more security

MFA takes the idea further. Instead of just two steps, it uses two or more different types of verification. These fall into three categories:

• Something you know (password, PIN).
• Something you have (phone, hardware token).
• Something you are (fingerprint, face scan).

For instance, logging into a corporate system might require:

1. A password (something you know),
2. A fingerprint scan (something you are),
3. And a code from a physical security key (something you have).

MFA’s flexibility makes it ideal for high-risk scenarios. Banks, healthcare systems, and government agencies use it because it’s tough for attackers to bypass multiple layers.

2FA vs. MFA: Which one do you need?

Understanding when 2FA and MFA work best and where they might fall short sets the stage for understanding which method fits into your security plan.

Let's see how these authentication methods shine in various scenarios.

When 2FA shines

2FA isn’t a one-size-fits-all solution, but in the right situations, it’s a game-changer. Let’s break down where it adds the most value:

Low-risk applications

For platforms that don’t handle highly sensitive data (think streaming services, newsletters, or forums), 2FA strikes a perfect balance.

It adds an important layer of security without overwhelming users. For example, a fitness app storing workout logs might use 2FA to protect user accounts but skip more complex MFA setups.

Customer-facing workflows

E-commerce sites, travel booking platforms, and subscription services benefit from 2FA because it secures transactions without disrupting the user experience.

Imagine a customer checking out on an online store: a quick SMS code or email OTP keeps their payment details safe without adding friction.

Challenges with 2FA

While 2FA is effective, it’s not flawless. Here are the hurdles businesses often face:

Implementation costs

Smaller businesses might struggle with the upfront costs of SMS gateways, authenticator apps, or API integrations. Maintenance also adds up — monitoring delivery rates, handling failed OTPs, and updating systems as threats evolve.

User resistance

Even a few extra seconds during login can frustrate users.

For example, if a verification code takes too long to arrive, customers might abandon a signup flow. Worse, some users may disable 2FA altogether if the process feels overly difficult.

Device dependency risks

If a user loses their phone or can’t access their email, account recovery becomes a headache. 

Support teams often field urgent requests like, “I changed my number, how do I log in now?” Without backup options (like backup codes or alternate verification methods), businesses risk locking out legitimate users.

Security gaps in SMS-based 2FA

SMS codes, while convenient, are vulnerable to SIM-swapping attacks or phishing. If a hacker hijacks a user’s phone number, they can intercept OTPs and bypass 2FA.

This is why industries like banking are moving toward app-based authenticators or hardware keys.

Accessibility issues

Not all users have smartphones or reliable internet. Relying solely on SMS or authenticator apps can exclude people in areas with poor connectivity or those using older devices.

Best practices for effective 2FA

To overcome these challenges, businesses need a strategic approach. Here’s how to make 2FA work for you, not against you:

Prioritize user education

Explain why 2FA matters. A short tutorial during signup (“This keeps your account safe from hackers”) or a friendly email reminder (“Your OTP is on the way!”) can reduce resistance. 

Transparency builds trust — users are more likely to comply if they understand the benefits.

Simplify the login experience

Simplifying the login process starts with small changes that make a big difference.

For example, let users sign in with their email address instead of forcing them to remember a complicated username. Since everyone already knows their email, this cuts down on forgotten login details.

Next, avoid overwhelming users with strict password rules. Yes, strong passwords matter, but forcing a reset every 30 days? That’s a recipe for frustration.

Pair 2FA with simple password guidelines (like a minimum length) to keep accounts secure without annoying users.

Offer multiple verification options

Not everyone wants SMS. Provide alternatives like:

• Email OTPs (for users without smartphones).
• Authenticator apps (like Google Authenticator for tech-savvy users).
• Backup codes (for emergencies when devices are lost).

Plan for account recovery

Don’t wait for lockout panics. Offer:

• Backup codes: Let users download or print them during setup.
• Alternate contact methods: Allow users to add a backup email or security questions.
• 24/7 support: Ensure users can quickly reach help if they’re stuck.

Balance security and convenience

For low-risk apps, allow users to “trust this device” for 30 days. This way, they only need to complete 2FA once per month on their personal devices.

How Plivo simplifies secure 2FA

Plivo helps businesses stop hackers in their tracks without adding friction for users. It combines strong security with seamless logins because staying protected shouldn’t be a hassle.

Here’s how.

Boost conversions with reliable OTP delivery

2FA only works if users actually get their codes. Plivo sends SMS and voice OTPs in under three seconds, so customers aren’t left waiting.

Here’s how it keeps things fast and secure:

• Automatically flags invalid or fake phone numbers.
• Routes messages through the fastest carrier to avoid delays.
• Sends millions of codes without slowdowns.

The result? Fewer abandoned carts, smoother sign-ups, and happier users.

Cut errors and fraud with phone number lookup

Plivo’s Lookup API does the detective work for you. It checks any phone number worldwide to spot:

• Fake numbers: Block accounts using disposable or invalid numbers.
• High-risk countries: Flag numbers from regions prone to fraud.
• Carrier details: Avoid sending codes to landlines (which can’t get SMS).

And the best part? It works in the background — no extra forms or user input needed.

Pricing starts at $0.004 per check, with bulk discounts for heavy users.

Save money with fewer failed messages

Plivo sends texts and calls directly to users — no detours, no middlemen. This “one-hop” system means:

• No extra fees: You’re not paying for undelivered messages.
• No delays: Codes arrive fast, every time.
• No surprises: Predictable pricing, even at high volumes.

For businesses, that means lower costs and fewer support tickets about “missing codes.”

Fraud protection built right in

Plivo also helps by:

• Blocking risky countries: Turn off SMS/calls to regions you don’t serve.
• Stopping premium number scams: Block calls to high-cost numbers hackers use to steal money.
• Giving pattern-based alerts: Get alerts if delivery rates suddenly drop (a sign of fake numbers).

When to consider MFA

Some logins are routine, while others protect something far more valuable. MFA isn’t always necessary, but in certain situations, it can be a smart safeguard. Here’s when it makes sense to use it.

Regulated industries

Financial institutions, healthcare providers, and government agencies often need to comply with strict requirements, mandating advanced security measures.

Critical systems

MFA is essential when securing servers, databases, admin panels, or tools that store intellectual property, customer data, or financial records. A breach in these systems can lead to significant financial and reputational harm.

Challenges with MFA

Implementing MFA comes with hurdles that require proactive management:

Device limitations

Older smartphones, legacy systems, or devices without biometric sensors (e.g., fingerprint scanners) may not support modern MFA methods like app-based authenticators or hardware tokens. Employees using unsupported devices risk being locked out.

Complex setup processes

Users often struggle with configuring MFA tools, especially without clear, role-specific guides. For example, setting up a YubiKey on a macOS device requires different steps than on Windows, and unclear instructions may lead to errors or abandonment.

Low user adoption

Without training, employees may view MFA as an inconvenience. Remote workers, in particular, might avoid using unfamiliar tools like biometrics or security keys if they lack guidance.

Security workarounds

Frustrated users may share passwords, reuse weak credentials, or disable MFA entirely to speed up logins — defeating the purpose of enhanced security.

Inconsistent user experience

Poorly integrated MFA solutions can disrupt workflows.

For example, requiring a hardware token, password, and SMS code for routine tasks slows productivity and fuels resentment.

Best practices for effective MFA

To tackle these challenges, businesses need a clear strategy. Here’s how to make MFA work in your favor:

Provide multiple authentication options

It’s important to offer a variety of authentication methods because not everyone uses the same devices or has the same tech skills.

Support diverse user needs and devices by offering:

• SMS/email OTPs: For employees with basic mobile phones or limited tech access.
• Authenticator apps: For users comfortable with smartphones.
• Biometrics: Fingerprint or facial recognition for modern devices.
• Hardware tokens: Physical keys for high-risk roles like system admins.

Implement role-based training programs

Training isn’t one-size-fits-all. Different teams have different needs, so your training should reflect that.

IT teams, for example, need detailed guides for setting up hardware tokens on different operating systems, like “How to Configure YubiKey on Windows 11.” Field employees, who might not have time for lengthy tutorials, benefit from short video demos showing how to approve logins via SMS or authenticator apps.

Executives, who often switch devices or travel, need personalized 1:1 sessions to make MFA setup hassle-free.

Enforce organization-wide MFA policies

If MFA is optional, attackers will target the weakest link. That’s why it should be enforced for everyone — employees, contractors, freelancers, and even legacy systems. Granting temporary access to third parties? They should use MFA too.

Outdated tools that don’t support MFA? Update or replace them. Regular audits help ensure no one is bypassing the rules.

Deploy adaptive MFA

Not every login needs the same level of security. Adaptive MFA adjusts based on the risk level of the situation.

For routine tasks, like checking email from a trusted device, a password and SMS code are enough. But for high-risk scenarios like logging in from a new country or accessing financial systems, additional checks, such as biometrics or hardware tokens, kick in.

Integrate MFA with single sign-on

Integrating MFA with single sign-on (SSO) enhances security without adding extra steps for users.

With SSO, they authenticate once using MFA and gain access to multiple systems without needing to log in repeatedly. This not only strengthens protection but also improves the user experience by reducing login fatigue.

Trends shaping authentication in 2025

As we move further into the digital world, the way we verify our identities is changing quickly. By 2025, new trends will change how people and companies confirm who they are and keep their information secure.

Here’s a look at the most important trends in authentication for 2025:

Passwordless authentication is on rise

Passwordless methods, like scanning your fingerprint, are faster and easier. Imagine logging into your account with a quick face scan instead of typing out a long password.

It’s not just convenient — it’s what people want. In fact, 52% of consumers prefer biometrics over traditional authentication methods. It’s no surprise this trend is picking up speed.

Businesses have also realized they need something better.

Already, 33% of IT departments globally are using passwordless authentication, and over one-third of companies are planning to adopt it in the near future. That’s a clear sign that companies see this as the future of security.

Plus, enterprises are already leading the way. For example, Microsoft reported 2 million monthly passwordless sign-ins through Windows Hello in early 2023. When tech giants like Microsoft push passwordless options, it’s a signal to everyone else: this is the direction we’re heading.

AI threats demand smarter defenses

AI-generated deepfakes are already posing serious security risks, and the problem is only growing.

By 2026, experts predict that 30% of businesses will no longer trust traditional identity verification methods due to the rise of AI-driven fraud. This is especially concerning for systems that rely on facial recognition or voice authentication, as deepfakes allow attackers to impersonate individuals and bypass security measures.

Financial institutions are particularly vulnerable — fraudulent identities can erode trust and compromise sensitive transactions. To counter this, businesses must adopt AI-powered tools capable of detecting deepfakes in real-time before they cause damage.

Beyond security, deepfakes also raise legal and ethical concerns.

As AI-generated content becomes more sophisticated, the reliability of biometric data declines, prompting discussions about data collection, storage, and privacy. Regulations similar to the General Data Protection Regulation (EU) (GDPR) may emerge to address these risks, ensuring stricter safeguards for personal information and reducing the potential for misuse.

Behavioural biometrics

What if your identity wasn’t based on a password you could forget or a fingerprint that could be faked, but on something uniquely yours — your online behavior?

That’s what behavioral biometrics is all about. It tracks how fast you type, the way you move your mouse, or even how you swipe on your phone to figure out if it’s really you.

This trend is picking up steam because it’s tough for hackers to mimic these unique habits, making it a powerful way to keep your digital life secure. Why is this so important?

Well, businesses and banks are jumping on board to protect millions of users. Take the Royal Bank of Scotland, for example. They’re using it to safeguard 18.7 million accounts by analyzing how people type and swipe.

It’s not just banks either; schools are using it to stop cheating in online exams by watching how students interact with their devices. The numbers back this up too — the market for this tech is projected to climb to $9.92 billion by 2030.

Decentralized identity

Picture this: instead of tech giants or governments holding all your personal info, you keep it in a secure digital wallet on your phone. That's a decentralized identity in a nutshell. So, why is this trend blowing up, and why should you care?

The rise is driven by a few key shifts. For one, cyberattacks are out of control. This makes those old-school, centralized databases look like easy targets.

People are also fed up with having no say over their data; they want to decide who sees what. Plus, new tech like blockchain is making this whole idea work better than ever. It’s no wonder the decentralized identity market is expected to jump from $156.8 million in 2021 to a massive $77.8 billion by 2031 — that’s serious growth.

With decentralized identity, you only share what’s needed, keeping your information safer. It also saves time (faster logins or ID checks) and works everywhere, from banks to online services.

Take the European Union’s EUDI Wallet: it’s already helping people access their data across borders without the hassle.

Future-proof your security strategy with Plivo

Whether you're protecting internal systems or customer data, understanding MFA vs. 2FA is important for your business. 2FA requires users to prove their identity in two simple ways. MFA takes it a step further by adding extra layers of verification for even stronger protection.

Using 2FA along with additional security checks gives you the best of both worlds: ease of use and solid security. Providers like Plivo simplify the process of setting up and integrating these tools, so you can protect your data without making things complicated for your users.

Contact us today to learn how our solutions can help secure your business.

McKinsey research shows that customer care leaders are shifting their focus across new themes.

They're moving beyond just customer experience to balance it with revenue targets and tech upgrades. At the same time, many are building future-proof, AI-enabled operational frameworks. Alongside these shifts, they’re investing in employee upskilling and deeper outsourcing collaborations.

This three-dimensional approach — customer satisfaction, profit contribution, and technological advancement — defines modern call center optimization.

That second one — AI-enabled operations is especially telling. It reflects a shift from reactive support to proactive, tech-driven efficiency.

This blog post outlines effective call center efficiency strategies, AI-aligned ideas, and key metrics to track, maximizing benefits from these advancements.

What is call center optimization beyond all the buzzwords?

Call center optimization is the strategic process of improving operational efficiency, agent productivity, and customer experience. It focuses on workflow refinement while reducing costs and enhancing service quality across all customer touchpoints.

While tracking metrics is a standard practice, effective optimization uses these metrics to boost profit margins, creating sustainable solutions rather than quick fixes.

Here are the four critical areas that call center optimization targets to deliver long-term business impact:

1. Agent performance: When you monitor and optimize agent performance, you’ll know which agents need product knowledge, soft skills development, or better tools to succeed.
2. Technology integrations: Proper tool integrations prevent data siloes, reduce repetitive tasks, and ensure your agents have complete information.
3. Customer satisfaction: Optimization ensures proper routing, agent training, and process simplification — the three key support aspects that enhance the customer experience.
4. Workforce management: Smart call centers use real-time data to match staffing with demand, improving coverage and efficiency.

The relation between call center optimization and your customer experience

Your key market differentiator is a top-notch customer experience, but the catch is to offer that without burning out your agents. Call center optimization creates a win-win by acing both. Let’s see how.

Streamlined call routing

Customers value speed above almost all other service factors. Each transfer or minute on hold ruins their perception of your brand.

AI-powered call routing optimization, like ticket classification and automatic routing, immediately connects callers to the right agent, saving agents up to 1.2 hours per day. This improves first call resolution rates and reduces abandonment.

Tailored customer service

Customers want personalized support, and 76% become frustrated when they don’t receive it. AI-powered call center solutions put customer data at agents' fingertips.

The solutions give your customer service representatives access to information like customer history, purchase patterns, and preferences on a single screen. This makes personalization possible even during high-volume periods.

Consistent service quality (across all channels)

Your customers switch between voice calls, email, chat, and social media to resolve the same issue; in fact, 37% use more than two channels. When you optimize for omnichannel support, customers receive the same accurate information, regardless of how or where they contact you.

Trained agents for proactive care

A survey found that 83% of consumers want companies to self-diagnose issues and contact them proactively before they have to reach out. 

With call center analytics and optimization insights, your agents can anticipate customer needs (say, customers at risk of churn), like sending reminders or addressing common issues via self-service.

First, assess your call center’s current state

Before you can improve your call center, you need a clear picture of your operation’s pulse. Tracking these metrics reveals strengths, exposes weaknesses, and sets a baseline for progress.

First call resolution (FCR) rate

How often do your agents solve issues on the first try? FCR matters because customers hate repeating themselves. A strong FCR rate (70-80%) cuts follow-ups, saves time, and keeps customers happy.

Check this by tracking resolved calls against the total first contacts over a week. Low numbers mean your agents might need better tools or training.

Average handling time (AHT)

This is the clock on each call — start to finish, including hold time and wrap-up. AHT averages 6 minutes and 10 seconds industry-wide, but shorter isn’t always better.

For example, a low AHT might indicate quick resolutions, but if paired with a low CSAT, it could suggest rushed interactions. Measure it daily to spot patterns (say, longer calls during peak hours) and adjust staffing or scripts accordingly.

Customer satisfaction score (CSAT)

CSAT, gathered from post-call surveys (typically on a 1-5 scale), tells you if your customers are walking away pleased. If your scores dip, learn why.

Generic post-call surveys (How satisfied are you?) often miss the point. Ask specific questions: Did the agent resolve your issue? Or were you transferred unnecessarily?

Schedule adherence

It’s the percentage of time call center agents are available to handle calls compared to their scheduled work time, accounting for breaks and other non-call activities. Low adherence means missed calls and frustrated customers.

So, track it weekly to catch chronic no-shows or overstaffing.

Call abandonment rate

This is the percentage of callers who hang up before speaking to an agent. A high rate can signal long waits or routing problems, which explains why they leave. Implement callbacks or improved interactive voice response (IVR) systems to reduce call center wait times and retain customers.

Net promoter score (NPS)

NPS measures customer loyalty by asking how likely customers are to recommend the company to others, typically on a 0-10 scale, with promoters (9-10), passives (7-8), and detractors (0-6). It’s a strong indicator of overall satisfaction and can predict business growth through word-of-mouth.

Start here. Measure these metrics now, and you’ll know exactly where to push harder.

Now, let’s begin optimizing your call center

You’ve assessed your call center’s current performance, and it’s time to improve it. Optimizing customer support centers is about smart, practical moves that deliver results.

Here’s how to get it done, step by step.

Step #1: Evaluate and establish technology integrations

To optimize your call center, start by equipping agents with tools that make their jobs easier.

Customer relationship management (CRM) software, smart call routing, and a unified desktop put everything agents need in one spot. Also, 81% of customers prefer self-service options for simple queries.

These tools can slash manual work and let your agents focus on what matters: solving problems. AI-driven customer service solutions help do that through specific functions, such as:

• Behavioral routing connects customers with agents who match both personality and skill requirements.
• Speech analytics shows agents real-time customer emotions, improving response appropriateness.
• Chatbots handle routine questions while collecting valuable CRM data (80% of consumers report positive or neutral chatbot experiences).

Plivo reduces live agent needs by 50% with Chatbots powered by OpenAI. The AI Self-Service Chatbot resolves queries instantly and handles tasks like exchanges and order tracking 24/7.

Integrated with your CRM system and knowledge base, it ensures brand-aligned responses. Escalate complex issues to agents via a unified desktop managing LiveChat, SMS, WhatsApp, and more for efficient, omnichannel support.

Step #2: Implement predictive analytics for proactive care

Predictive analytics help you stay ahead of the curve through smarter planning. It lets you dig into past data like call volumes, busy hours, and even seasonal spikes to present forecasts that let you staff right.

No more overworked agents or idle hands — analytics also guide staff schedules, meaning you’re ready for that holiday rush or surprise campaign surge.

You can also spot customer patterns, such as who’s likely to call about billing, prep agents with answers, and cut handle times.

Plivo’s Proactive Service anticipates customer needs, slashing inbound support with automated workflows triggered via API. Deliver answers before questions arise through SMS (98% open rate) and seamless live-agent handoffs, creating loyal customers.

Step #3: Use metrics and reporting to make decisions

Metrics and real-time reports show you what’s working and what’s not. Tracking key stats like average handle time, FCR, and customer satisfaction in real time helps you spot issues quickly.

With these insights, you can create targeted knowledge base articles and training for those issues.

But reporting has evolved. Traditional reporting looks backward at problems that have already affected customers. Today’s AI-powered monitoring catches issues as they form. Your agents fix these issues before most customers experience them, with 92% of CRM leaders saying their customer response times have improved.

Plivo’s Metrics & Reporting supercharges your call center decisions. Monitor queues, operations, and agent performance with real-time dashboards. Dive into historical reports for deep insights to improve your agent coaching.

Step #4: Train your agents with updated information

Agents aren’t mind readers. When they lack the tools and training they need, they struggle to perform their daily chores, leading to burnout.

Turnover is brutal, too: It takes three to four times an employee’s salary to replace them. So invest in their upskilling and see consistent results.

Hit them with fresh product details and customer insights (think role-plays on new features or breakdowns of common complaints). Equip them with cheat sheets updated monthly, not yearly. Skilled agents mean faster fixes and happier callers every time.

Apart from offering live metrics and historical insights, Plivo lets you access tools like call recording, barge, whisper, and CSAT collection to support targeted training and boost loyalty and call center performance. Your agents can also have a custom knowledge base that is fully integrated with the business systems.

Step #5: Create feedback loops (from agents and customers)

Who better than your agents to know what's broken in your call center before your metrics show it? They hear customer frustrations directly, experience system limitations daily, and develop workarounds for inefficient processes.

Your agent feedback loops require:

• Weekly team huddles where agents share recurring customer pain points.
• Anonymous suggestion systems for process improvements.
• Post-call surveys asking specific questions about tool effectiveness.

Customer feedback needs similar attention. In fact, 31% of customer service leaders analyze customer feedback as their strategy for providing excellent experiences.

An effective customer feedback loop should include:

• Targeted post-call surveys: Ask specific questions to understand the customer’s experience, not just general satisfaction.
• Call transcript analysis: Identify patterns, recurring complaints, and common friction points.
• Thematic tagging: Organize feedback into themes to highlight the most critical issues.
• Prioritized action: Focus on changes that address high-impact problems or recurring concerns.
• Transparent follow-up: Let customers know what was improved based on their feedback to build trust and show accountability.
• Continuous refinement: Keep the loop active by regularly reviewing feedback and adjusting processes or tools accordingly.

The power of AI in successful call center optimization

AI is changing how call centers operate, making them faster and more responsive. The global call center AI market hit $2 billion in 2024 and is set to grow at 23.8% yearly through 2030.

Experts at Gartner forecast that by 2025, 80% of customer service teams will use generative AI to boost agent efficiency and improve customer experiences.

Lately, industry buzz has highlighted AI’s growing role in streamlining operations. Many are eyeing automated solutions to handle routine tasks while keeping skilled agents for tough challenges.

Here’s how AI delivers value:

• Faster call wrap-ups: AI handles the grunt work of summarizing calls. It pulls key details (like complaints or follow-ups) from recordings and turns those talks into text transcripts. This cuts agent wrap-up time, with summaries and transcripts auto-added to customer records, saving effort.
• Smarter live chat: AI agents now handle chats with ease, tackling both simple and complex queries. Unlike basic bots, these agents act autonomously, offering 24/7 support, answering questions, and even scheduling meetings without human intervention.
• Better quality checks: AI-powered quality assurance tracks every interaction, spotting issues fast. It helps call center managers update processes and improve service without manual reviews, ensuring consistent quality.
• Conversational IVR: Forget rigid phone menus. AI-driven IVR uses natural language processing (NLP) to let callers explain issues in their own words. It routes calls or solves problems directly, making the experience smoother.

Implement your AI-powered contact center with Plivo

Plivo CX offers a full-featured, omnichannel solution with top-notch security to move your call center operations to the cloud.

The service packs everything you need: a single dashboard for managing omnichannel tasks, OpenAI-built self-service chatbots, a no-code workflow builder, and real-time reporting.

Plivo CX brings:

• A Unified Agent Desktop to manage calls, chats, and emails, cutting the hassle of switching channels.
• Chatbots are trained on your data to handle simple issues, leaving agents for tough cases.
• Analytics to read customer emotions in real time, helping agents respond better.
• Integration with your CRM, enterprise resource planning (ERP), or helpdesk software for a complete customer view.
• A drag-and-drop workflow builder to craft custom communication flows across channels.
• Smart routing to send queries to the right call center team, speeding up resolutions.

Plus, you can train the chatbots with your own data for spot-on answers. Contact us to book a demo today.

But don’t just take our word for it.

Businesses across industries are seeing improvements after implementing Plivo's solutions. Here’s what Scott Rosen, President and CEO of MDabroad, has to say about us:

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Are you an e-commerce business owner watching your support team drown in repetitive questions? Frustrated by customers abandoning carts because they couldn't get help fast enough? 

These pain points aren't just annoying—they're costing you revenue and growth opportunities every day.

However, custom AI agents are the breakthrough solution you've been searching for.

They constantly learn from your data, adjust to your tone, and respond like someone who knows your brand inside out. This makes every interaction feel personal, fast, and relevant. 

In this blog, we introduce you to custom AI agents, their benefits, types, and how they operate. We’ll also discuss how e-commerce businesses use custom AI agents for support with real-life AI agent use cases, recommendations, and engagement, and how you can do the same.

What are custom AI agents?

Custom AI agents are independent AI systems that utilize machine learning and natural language processing to interact with customers, process critical information, and perform specific tasks. 

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Typically, they are more effective than pre-built solutions as they can adapt to a business’s unique needs, offering more personalized and efficient support.

Benefits of custom AI agents in customer service 

As more e-commerce businesses look to personalize and scale their customer experience, custom AI agents are proving to be a smart investment. Here are some interesting findings:

• According to Gartner, by 2025, AI and automated systems are expected to handle 75% of all customer service interactions.
• It also predicts that by 2027, chatbots will become the primary customer service channel for approximately 25% of companies.
• At the same time, in McKinsey’s March 2025 Global AI Survey, 78% of respondents report using AI in at least one business function—up from 55% a year earlier. 

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Let’s go over some of the benefits of custom AI agents for your business:

Hyper-personalization at scale

Comvia’s research states that 56% of companies struggle to deliver real-time personalization, often due to disconnected systems and limited staff capacity. 

Custom AI agents can solve this problem by accessing customer data instantly, including purchase history, preferences, and behavior, and using it to tailor conversations in real time. 

These agents can deliver the personalization your team may not have time for at scale.

Cost and time savings

Salesforce data shows that 95% of decision-makers at companies using AI report reduced costs and time savings, and 92% say generative AI improves customer service.

Custom AI agents can take over high-volume, low-value tasks like answering FAQs, tracking orders, and routing queries. That means fewer support tickets for your human team and faster customer resolution.

Increased customer retention and loyalty

AI-powered systems have driven a 31.5% increase in customer satisfaction scores and a 24.8% boost in retention rates. AI agents make customers feel heard and valued by remembering past interactions, resolving issues quickly, and offering relevant suggestions. 

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And when customers get the support they need without repeating themselves or waiting in long queues, they’re more likely to stick around. This means higher loyalty, fewer churned customers, and more repeat purchases without adding pressure to your support team.

Enhanced data insights and feedback loops

AI agents can track customer behavior, identify recurring issues, and automatically flag gaps in service. That means fewer blind spots and faster responses to changing customer needs. 

These continuous feedback loops translate to smarter product decisions, more effective campaigns, and higher customer satisfaction, all without adding complexity or headcount.

Scalability for growing businesses

Custom AI agents enable e-commerce businesses to scale customer support efficiently without proportionally increasing staff. 

As a case in point, Swedish fintech company  Klarna’s AI chatbot manages two-thirds of its customer service inquiries, effectively performing the work equivalent of 700 full-time agents. This implementation has led to a projected $40 million profit improvement in a single year.

Types of custom AI Agents

Custom AI agents come in different forms, each built for a specific function, industry, or customer need. Here’s a quick breakdown of the most common types :

Conversational agents

These AI-driven tools engage users through natural language processing to carry out conversations. They’re widely used in virtual assistants, chatbots, and customer support to deliver human-like interactions.

Recommendation engines

These agents suggest products, services, or content based on user behavior and preferences. They help personalize experiences and are widely used across e-commerce sites, streaming platforms, and social media.

Predictive analytics agents

Used in industries such as finance, healthcare, and marketing, these agents analyze historical data using machine learning to forecast trends and inform strategic decisions.

RPA (Robotic Process Automation) agents

These agents are designed to automate routine tasks such as data entry and transaction processing, help organizations reduce manual effort, improve accuracy, and boost productivity.

Personalized learning agents

These AI agents are focused on education, adapting to individual learning needs, tracking progress, and enhancing online learning platforms to improve educational outcomes.

How AI agents work

To understand what makes custom AI agents effective for customer engagement, it’s helpful to look at how they operate behind the scenes. Here’s how it works:

Perception

AI agents collect input from various sources, such as text messages, voice commands, clicks, or behavioral patterns. With technologies like natural language processing (NLP) and computer vision, they interpret this data to understand what the user is saying or doing.

Reasoning

Once the data is processed, the agent evaluates the context and intent. It draws from its training data, business rules, or decision trees to determine the most appropriate response or action. This is where logic and analysis come into play.

Action

Based on its reasoning, the agent carries out an action. This might be replying to a customer, triggering a workflow, offering a recommendation, or escalating the issue to a human agent. The goal is to resolve the task efficiently and accurately.

Learning

After each interaction, the agent stores new data and feedback. Over time, it utilizes this information to refine its responses, enhance accuracy, and adjust to evolving customer behavior. 

This continuous learning loop makes AI agents increasingly useful the longer they are in use.

Key use cases of custom AI agents in E-commerce

From supporting customers around the clock to sending personalized product suggestions, custom AI agents can adapt to your workflows and goals. Here are some of the most effective ways e-commerce businesses are putting them to use:

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AI-driven customer support

• 24/7 availability
  Custom AI agents can handle customer queries at any time of day without human intervention. For example, footwear and apparel company Allbirds uses AI-powered chat support to assist customers with order tracking and FAQs around the clock.

• Context-aware query resolution
  These agents remember previous interactions and use that data to personalize responses. A user asking about a return status today will get an informed response based on their chat from last week.
  Zappos, for instance, has integrated AI into its support system to handle returns by recognizing purchase history and context automatically.

• Multilingual and omnichannel capabilities
  
  Custom AI agents can engage with customers in multiple languages across platforms like websites, mobile apps, and social media. A great example is H&M, whose AI chatbot supports users in different languages and on platforms like WhatsApp and Facebook Messenger, ensuring consistent global support.

Personalized product or content recommendations

Based on user behavior, purchase history, preferences
AI agents can analyze browsing patterns, past purchases, and user preferences to recommend real-time products and services. Amazon’s AI recommendation engine accounts for 35% of total revenue by suggesting products tailored to each user.

• Dynamic adaptability and learning
  
  Custom agents continuously learn and update recommendations based on user behavior. Stitch Fix employs AI to analyze customer preferences, sizes, and feedback. This enables the platform to deliver personalized clothing recommendations, enhancing customer satisfaction and engagement. 

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Automated engagement strategies

• Targeted messages based on the customer journey stage
  
  Custom AI agents can send different messages depending on where a customer is in the funnel, such as welcome emails for new users, product demos for engaged users, or discount codes for those at risk of churning.
  
  Sephora uses AI to send personalized birthday rewards and product suggestions based on purchase history and browsing behavior.

• Proactive outreach (cart reminders, renewal alerts, etc.)
  
  AI agents take the initiative instead of waiting for the customer to act. If someone abandons a cart, the agent can automatically send a reminder with an incentive.
  Bonobos uses AI-powered reminders to prompt repeat purchases and send personalized alerts about upcoming deliveries or product renewals.

AI-Powered chatbots with custom personas

• Tailored tone, voice, and personality
  
  Custom AI agents can be trained to reflect your brand’s tone, whether friendly, formal, or quirky.
  For instance, Casper introduced an AI chatbot named Luna 2.0. This chatbot uses a friendly and conversational tone to assist customers with their purchases, making the interaction feel more human and aligned with the brand’s approachable identity. 

• Cross-platform engagement (web, app, messaging platforms)
  
  These agents can live across multiple channels and maintain the same tone and context everywhere.
  Walmart has developed an AI-powered voice shopping assistant that integrates with Google Assistant, Siri, and Google Home devices. This allows customers to interact with Walmart’s services using natural language across various channels, ensuring a unified and personalized shopping experience.  

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Best practices to implement custom AI for businesses

With AI adoption on the rise, many businesses are still figuring out how to get it right.

A recent IBM survey shows that 35% of enterprise IT professionals use AI specifically to improve customer service agent productivity. In contrast, many others rely on it to enhance overall customer and employee experiences.

To make the most of your investment in custom AI agents, here are some practical best practices to follow:

Follow AI ethical principles

Building trust with your customers begins with the responsible use of AI. 84% of IT professionals agree that consumers are more likely to choose services from companies with transparent and ethical AI practices. 

This means being transparent about your AI agents’ decisions, avoiding biased responses, and protecting customer data. 

Promote human-AI collaboration

A recent study by the National Bureau of Economic Research found that customer support agents using a generative AI assistant increased their productivity by 14% on average. 

Notably, less experienced and lower-skilled workers saw the most significant improvements, highlighting the importance of human-AI collaboration.  Businesses can enhance performance by designing AI systems that complement human skills while ensuring quality and accountability. 

Prepare internal data

A survey by Hitachi Vantara revealed that 37% of U.S. IT leaders identify data quality as a major barrier to AI success. Yet, many organizations are not adequately enhancing their data management practices. 

To maximize the potential of custom AI agents, invest in robust data infrastructure, ensure consistent data tagging, and regularly review datasets for accuracy and completeness.  

Support ongoing training

AI agents improve over time, but only if you actively manage them. Regularly updating training data, monitoring performance, and incorporating customer feedback are essential to fine-tune their responses. 

According to an IBM study, 40% of employees will need to retrain due to AI implementations, highlighting the importance of continuous learning and human oversight in AI deployment.  

This will ensure better accuracy, relevant interactions, and customer satisfaction.

Measure and evaluate

According to PwC’s Future of Customer Experience report, 73% of consumers consider the experience an important factor in their purchasing decisions, and 65% of U.S. customers find a positive experience with a brand to be more influential than great advertising.  

By consistently monitoring and refining key metrics like resolution time, deflection rate, and customer satisfaction (CSAT), businesses can ensure their AI agents contribute to improved customer satisfaction and loyalty.

Use Plivo’s custom AI agents to engage customers before they ask 

Custom AI agents are a smart investment for brands looking to grow without compromising customer experience. If you’re ready to put AI to work for your business, Plivo can help. The platform’s custom AI agents are built to fit your workflows, learn from your data, and engage your customers the way you do. 

Here’s also what you get with Plivo to build a solid customer experience:

• Unified agent desktop: A single platform for managing all channels, enabling agents to respond swiftly and accurately while having the full context of each interaction
• AI voice agents: Human-like interactions with advanced speech recognition and contextual awareness
• Seamless integration: Works seamlessly with your CRM, billing, ticketing tools, and knowledge bases to ensure consistent, informed responses
• 24/7 self-service chatbot: Powered by OpenAI, the chatbot addresses up to 70% of common queries, allowing agents to focus on more complex cases
• Automated workflows: Handles follow-ups, routes tickets, and provides updates automatically to keep customers informed
• Intelligent handovers: When AI fails to respond to complex cases, it escalates the query to a human agent with complete context, speeding up resolution
• Top-tier security: Adheres to SOC 2 and GDPR standards to ensure secure customer communications

Want to boost your revenue with custom AI agents? Book a demo now.

Needless to say, ​customer support is a vital organ for business success, directly affecting customer satisfaction, loyalty, and revenue. In fact, a study notes that in 2024, poor customer experiences cost organizations worldwide an estimated $3.7 trillion, marking a 19% increase from the previous year.

Customers often switch companies if they don't receive good customer service, regardless of product satisfaction. Conversely, they are often willing to go out of their way to do business with a company that offers better service. 

Support agents are at the forefront of delivering these sensitive experiences, making their role crucial in fostering customer relationships and driving business growth.​

In this article, we’ll explain who they are, what they do, the essential skills needed to become one, and some steps to build your support team with the best support agents.

Who are support agents (and who are they not)?

A support agent is a trained representative of your company primarily tasked with resolving customers' complaints, answering inquiries, and providing usage guidance on your company’s products and services.

Beyond handling tickets, they also provide emotional support by ensuring customers feel heard, valued, and reassured throughout the interaction. This, in turn, strengthens customer trust and loyalty.

Support agents play a vital role in enhancing customer satisfaction—which indirectly drives ROI through repeat purchases—but they are not sales representatives or marketers. Marketers and sales reps acquire customers and sell. 

On the other hand, support agents retain these customers and boost repeat purchases through efficient assistance.

4 primary responsibilities of a support agent

A support agent's primary responsibilities include serving as the first point of contact, handling queries, providing proactive support, and gathering essential feedback.

1. Serving as a brand's first-line contact

When things go wrong, customers become frustrated and demand to speak with anyone—even the CEO. Your support agents step in to act as the first line of defense, de-escalate situations, and provide solutions professionally. 

They also represent your brand through every empathic response, ensuring customers leave with a positive experience and a lasting impression of excellent service.

2. Handling customer inquiries

Customer support agents handle all customer inquiries. Depending on the technicality of the query, they might sometimes involve a specialist or higher executive. 

For instance, if a customer has a dispute over billing that involves policy exceptions, your agent might need to coordinate with or escalate to a higher executive for approval.

3. Providing proactive support

Support agents detect customer needs ahead of time and promptly contact them even before they log a complaint. This helps reduce inbound tickets and boosts customer satisfaction. 

As an omnichannel customer support platform, Plivo CX’s proactive service enables your support team to provide a more refined and automated proactive support.

4. Gathering feedback to optimize product offerings and marketing

Your support agents are the most valuable source of feedback since they interact with customers daily. They can help you gather recurring concerns, pain points, and suggestions that drive meaningful product improvements. 

Plivo CX’s metrics tool automates this feedback collection process and ensures your agents focus on core tasks instead.

Essential soft skills of a good support agent

Being a support agent involves not only resolving queries but also understanding and communicating with customers on a personal level. This is why certain soft skills, beyond technical competency, are essential when recruiting one.

Active listening and empathy

Active listening involves paying close attention to every word and emotion communicated rather than focusing solely on the problem. 

Unsurprisingly, active listeners excel at expressing empathy since they’re more likely to understand how customers feel better. This deeper connection allows them to communicate effectively, provide reassurance, and de-escalate tense situations.

Good communication skills

A good support agent must convey their points concisely while considering the appropriate tone. 

They know when to avoid passive-aggressive words such as “actually, ought to,” colloquialisms, and slang to maintain clarity.

Adaptability

Different customers present with different complaints, personalities, and communication styles. Some are patient and easy to communicate with, whereas some are aggressive and impatient. 

So, a one-size-fits-all approach won’t work, and that’s why an ideal support agent must be able to adapt to each high and low while maintaining efficient support delivery.

Problem-solving skills

Customer support playbooks are valuable, but not every customer complaint will fit neatly into predefined solutions. Situations like this require your support agent to think outside the box while staying within your company’s guardrails. 

A good support agent must be able to devise quick solutions to new problems and escalate as soon as possible when necessary.

6 steps to hire the right support agent and build a functional support team

Your support team can either make or mar your business. Hiring the right agent and building a functional team is, therefore, essential. Let’s see how to do that.

1. Define your staffing needs and ideal agent

You can figure out how many support agents to hire based on your historical ticket volume, scale of business expansion, and anticipated growth in the next quarters. This ensures you don’t over-hire and bloat your expenses or under-hire and cripple your support delivery. 

Plivo CX’s reporting and metrics tool provides a comprehensive ticket summary, which helps you determine your historical ticket volume.

Simultaneously, you need to define who your ideal agent is—that is, the skills they should preferably possess, availability, technical proficiency, lingual capacities, and experience. You can diversify your recruitment process if you’re catering to multilingual audiences.

Once you’ve identified your staffing needs and ideal candidate profile, you can post your job listing on your company’s career page and on popular job boards like LinkedIn to attract qualified applicants.

2. Prioritize agents with empathy and problem-solving skills

You need a team of agents who can dialogue with different kinds of customers, resonate with them emotionally, and make them feel heard while providing adequate support. That’s why you should prioritize agents with empathy.

Also, look for agents with strong problem-solving skills—someone who can think quickly on their feet and provide solutions to out-of-the-script problems.

Tools like TestGorilla and Testify help you assess your applicant’s skills and ability to address different scenarios. For a more tailored assessment, present candidates with real scenarios or past customer complaints from your database and ask them to resolve the issue.

3. Test for relevant technical competency

Technical competency is your support agent’s ability to resolve customers’ technical queries. This could include troubleshooting software issues and providing answers to product-specific technical queries.

Usually, your agents don’t need to be tech geeks or specialists for L1 tickets. An entry-level customer support agent with the necessary soft skills can do a great job here.

However, it’s a different ball game for agents managing L2 and L3 tickets. They should have the necessary expertise to manage complex and product-related technical issues like specialists. 

This primarily hinges on their hard skills portfolio, which you need to consider when hiring. Provide real-world scenarios to assess these skills and ensure they genuinely have what it takes to be your ideal agents.

4. Provide comprehensive agent training

From the onboarding stage, you need to identify the gaps in your hires’ competencies and design individualized or group training to boost their efficiency. Training can include soft skills, lingual capacity, and upskilling hard skills for more proficiency.

Agent training is not a one-time process. Plivo CX’s coaching tools help your agents leverage past interactions with customers to continuously refine their delivery—how to respond or handle similar situations when they arise again.

5. Equip your support agents with AI-powered tools

Gartner notes that more than 80% are either using or planning to integrate AI-powered chatbots in 2025. Advanced chatbots are essential because they can handle the majority of L1 tickets, thus freeing up your agents to do other complex tasks.

An example is Plivo CX’s openAI-powered AI chatbot, which can leverage your database in real time to make decisions, autonomously handle refund requests, modify orders, and make personalized recommendations.

Your team also needs an omnichannel platform that centralizes all communication channels into a single dashboard. This allows agents to access everything they need without constantly switching tabs, ultimately improving response time and efficiency.

An example is Plivo CX’s unified agent desktop designed to eliminate data silos, unify all incoming customer requests, and integrate with homegrown systems, CRM, ERP, helpdesk, and more.

6. Establish KPIs and reasonable expectations

Establish KPIs, such as first response time, average resolution time, and first contact resolution, to assess the efficiency and effectiveness of your support agents. 

Customer Satisfaction Scores (CSATs) will help determine whether customers are getting the help they need and how they need it.

Plivo CX’s agent metrics and reporting tool enables you to monitor and measure your support agent’s performance while exposing gaps that need filling.

You also need to set individualized, SMART expectations and goals for your support agents. Ensure each agent does not handle more tickets—whether in quantity or complexity—than they can. This will reduce the incidence of customer service burnout and enhance the quality of your support delivery.

Scale your customer support operations with Plivo CX

Hiring the right support agent is just one part of building a functional customer support team. You also need to provide them with the right tools to enhance their efficiency and effectiveness.

That’s where Plivo CX comes in. 

As an omnichannel platform designed to streamline support delivery, Plivo CX offers a unified agent desktop to centralize your support channels, multilingual AI-powered voice support, and OpenAI-powered chatbots to slash your ticket queue.

We also offer: 

• Seamless integrations: Connect Plivo CX with your existing tools, including CRM, ERP, helpdesk, and more.
• Data-driven optimization: Track real-time analytics and generate custom reports to gain insights into customer interactions and agent performance.
• AI-enhanced efficiency: Prioritize urgent tickets, ensure brand consistency, and adapt quickly to changes with AI-powered support.
• Rich communication: Enhance interactions with multimedia support, including images, videos, and documents.
• Increased productivity: Streamline workflows with message templates, internal notes, and skills-based routing.
• Comprehensive features: Leverage call recording, IVR, multi-language support, and robust security features for a complete solution.

‍Book a demo today and start building a world-class support team with Plivo CX.