Unlike a one-time chatbot response or a static automation script, AI agents represent a growing class of intelligent systems that can break down complex tasks, interact with multiple tools, collaborate with other agents, and iteratively improve their own output. They aren’t just executing commands — they’re reasoning, planning, and adapting in ways that mimic human workflows.

In this article, we’ll explore what AI agents really are, how they differ from traditional AI use, and why they’re critical to the next evolution of software. We’ll also delve into agentic workflows, multi-agent systems, and the practical frameworks that developers and businesses can use today — even with no code.

What Are AI Agents? Separating Hype from Reality

Defining an AI agent may sound simple, but in reality, it’s a fast-evolving field where boundaries are still being explored. At its core, an AI agent is a system that doesn’t just respond to a single prompt — it acts, reflects, and improves over time by interacting with its environment, tools, and other agents.

Beyond One-Shot Prompts

A traditional AI interaction might look like this: “Write an essay about climate change.” The AI responds with a coherent answer, but it’s static — there’s no reflection, iteration, or adjustment based on feedback.

An AI agent, by contrast, approaches the task as a process. It might:

• Start by outlining key points.
• Check for gaps or conduct research using a web tool.
• Draft a version of the essay.
• Critically review and revise it.
• Finalize the output based on internal logic or collaborative feedback.

This circular process — think, do, reflect, refine — is what distinguishes an agentic workflow from traditional one-shot interactions.

The Agentic Ladder: From Prompts to Autonomy

There are levels to this new AI behavior:

• Basic Prompting — A single request yields a single response. No iteration.
• Agentic Workflow — The task is broken into sub-steps, revisited iteratively.
• Autonomous AI Agents — The system independently determines goals, tools, and workflows, improving over time without human guidance.

While we’re not yet at full autonomy across all domains, many AI systems today already function at level two, thanks to breakthroughs in agent design and tool integration.

Four Core Patterns of Agentic Design

To understand how AI agents function, it’s helpful to look at four widely accepted agentic patterns:

1. Reflection

Reflection is when an AI reviews and critiques its own output. For example, after writing code, it can be instructed — or prompted by another AI — to check for logic errors, inefficiencies, or style issues. This creates a feedback loop, enabling improvement.

2. Tool Use

Agents equipped with tools can perform tasks that go beyond language. For instance:

• Search the internet for real-time information.
• Use a calculator or code interpreter.
• Access email and calendars to schedule events.
• Perform image generation or recognition.

By integrating tool use, AI agents become far more capable than static chat interfaces.

3. Planning and Reasoning

Planning agents can break a high-level task into smaller sub-goals and determine which tools to use at each stage. For example, generating an image based on pose recognition from a reference file involves multiple steps — each potentially executed by different models or tools.

4. Multi-Agent Collaboration

Inspired by human teams, multi-agent systems distribute tasks across specialized agents. Rather than one model doing everything, different agents handle writing, editing, researching, coding, or decision-making. Collaboration and role specialization lead to more accurate, efficient, and modular workflows.

Multi-Agent Architectures: Building Smarter AI Teams

A single agent can be powerful, but a group of agents working together — like a well-organized team — unlocks new levels of performance. Based on insights from Crew AI and DeepLearning.AI, we now have several design patterns that underpin these collaborative systems:

Sequential Workflow

Agents pass tasks down a pipeline, like an assembly line. One extracts text, the next summarizes it, another pulls action items, and the final one stores the data. This is common in document processing and structured automation.

Hierarchical Agent Systems

Here, a manager agent assigns tasks to subordinate agents based on their specialties. For example, in business analytics, one sub-agent may track market trends, another customer sentiment, and another product metrics — all reporting to a decision-making agent.

Hybrid Models

In complex domains like autonomous vehicles or robotics, agents operate both hierarchically and in parallel. A high-level planner oversees route optimization, while sub-agents continuously monitor sensors, traffic, and road conditions, feeding updates in real time.

Parallel Systems

Agents independently process separate workstreams simultaneously. This is especially useful for data analysis, where large datasets are chunked and processed in parallel before merging results.

Asynchronous Systems

Agents execute tasks at different times and react to specific triggers. This is ideal for real-time systems like cybersecurity threat detection, where various agents monitor different aspects of a network and respond independently to anomalies.

No-Code Agent Development: Building an AI Assistant with N8N

The power of agents isn’t limited to expert coders. Platforms like n8n enable anyone to build multi-agent systems using drag-and-drop workflows. For example:

• An AI assistant on Telegram named InkyBot listens to your voice or text.
• It converts voice to text using OpenAI’s transcription.
• It interprets your message, checks your Google Calendar, and helps prioritize tasks.
• It then schedules new events, updates you, and continues the conversation — all without code.

This workflow mirrors the T-A-M-T model (Task, Answer, Model, Tools):

• Task: Prioritize tasks for the day.
• Answer: A to-do list and scheduled calendar events.
• Model: GPT-4 (or any compatible LLM).
• Tools: Calendar APIs, transcription services, messaging platforms.

As simple as this example is, adding more agents or tools can result in highly advanced personal assistants, customer service bots, or research analysts — all built without writing a single line of code.

Opportunities: Why AI Agents Are the Next SaaS Boom

One of the most compelling takeaways from AI agent development is this: for every traditional SaaS product, there’s now the opportunity to build its AI-agent-powered counterpart.

Instead of a project management platform, you can build a task delegation agent that manages human and AI workflows. Instead of a customer service dashboard, you can create an agent that triages, replies to, and escalates tickets. Think of verticalized AI agents for:

• Travel planning
• Content marketing
• Investment analysis
• Health tracking
• Legal document review

If you want to build something useful with AI, simply identify a SaaS product and envision how it could be transformed into an autonomous, intelligent agent-based workflow.

Challenges and Considerations

While AI agents are powerful, they also introduce new complexities:

• Error propagation: Mistakes made early in a workflow can cascade.
• Debugging: Multi-agent systems are harder to troubleshoot than single-model tools.
• Interpretability: With autonomous decision-making, it can be difficult to understand why an agent made a choice.
• Security: Agents accessing tools (like email or calendars) must be tightly governed to avoid misuse.

Still, with robust design, transparency, and human-in-the-loop supervision, these concerns can be addressed effectively.

Conclusion: Welcome to the Age of AI Agents

We’re entering a new era in artificial intelligence — one where machines don’t just respond to requests, but independently break down tasks, collaborate, and adapt. AI agents offer a compelling bridge between static automation and general AI. They empower us to build systems that can reason, plan, reflect, and even work in teams.

Whether you’re a solo entrepreneur, a researcher, a developer, or just an AI enthusiast, this is the moment to explore what agents can do. With the right tools and mindset, you can build intelligent systems that automate the unthinkable and unlock a new dimension of productivity.

And best of all — you don’t need to code to get started.

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Virtual assistant technology lets you perform tasks or access services through your voice commands over a mobile or desktop. A Voice assistant is a software agent who reacts to a command received from the user and gives back the relevant information in response to his inquiry.

Voice assistance gives users hands-free access to various functions as they only interact with voice. Voice assistance can be used to remove the language barrier while interacting with information on the web.

Voice user interfaces, powered by virtual assistants, are currently ubiquitous in automobiles, computer operating systems, home automation systems, home appliances such as washing machines and microwave ovens, and television remote controls.

Voice assistants such as Google Assistant and Alexa have become household names. Many tasks, such as creating a reminder for any particular date and adding items to a shopping list, can be done with a simple voice command. Although voice recognition technologies have been introduced since the 1960s, some more advanced voice or virtual assistants have been introduced only in the last decade.

Use cases of Virtual Assistants

• Task Automation: Virtual Assistants can be used to automate processes in medical labs where everyone is required to gloves and bodysuits to prevent contamination. It can also be used to set reminders for future events or appointments. Many household devices have become intelligent such as smart TV, smart AC, smart lighting, smart Fridge, and much more. All these devices can be easily controlled with a virtual assistant.
• Reduce Screen Time: Virtual Assistants allows the users to perform various tasks hands-free and without even interacting with a device screen. Some of the functions that can be achieved are making a call to someone, opening apps on your phone, reading out messages and emails verbally instead of reading them, finding local shops and businesses of your interest, speaking out messages or emails, and converting it to text instead of manually typing it, creating and maintaining a to-do list, stream music, get sports scores, get weather updates and many more such tasks can be performed without even a need for a physical device.
• Provide Aid to the Visually Impaired: Virtual Assistants are hands-free technology and don’t require the user to physically interact with the interface it can be used as a guide to visually impaired people. It can fully utilize the most valuable features of a phone to the visually impaired person, such as tracking GPS location, automatically reading out messages along phone number or name of the sender, the time and date, reading out system information like battery level, and much more.
• Remove Language Barrier: With Virtual Assistants, companies can overcome language barriers and easily do business with a user base that speaks a different language. Virtual Assistants like Alexa and Google Assistant already provide support for multiple languages. Also, language translation technologies are usually free of cost and can be easily integrated with any service. In 2019 Google launched the interpreter mode, which enables two users to communicate in a different language with real-time translation. As of now, this feature supports about 27 languages, with a plan to include more languages in the future.
• Predictive Analysis: Virtual Assistants can analyze user interactions and patterns and suggest related things according to the history of interactions to provide a more personalized experience. Organizations that offer services through voice assistants can use the assistant to analyze historical user data and suggest changes accordingly to increase profits using this data.
• Support for eCommerce: In a large organization, providing customer support to a large customer base can sometimes become very expensive and unfeasible. Using Virtual Assistants, you can automate tasks such as taking user queries and customer feedback. As interactions are more conversational than graphical, it provides the user with a more engaging experience. Also, unlike humans, a Virtual Assistant would never take a break; hence can be available to users throughout the day.

Limitations and security concerns

Virtual Assistant Technology is becoming more and more sophisticated every day. But this doesn’t mean that they will replace humans altogether. The number of Virtual Assistant users increases daily; some concerns are being raised about the Security and Privacy of the users.

• Privacy concern: When a user gives a command to the Virtual Assistant. The voice command is recorded and sent to the server for further processing. So ideally, every Virtual Assistant has a wake work which, when spoken, activates the assistant. For Google Assistant, it’s “Ok, Google,” For Alexa, it is simply “Alexa.” When the wake word is spoken, the assistant starts the conversation and the recording. A common misconception is that the Virtual Assistant continuously records the user’s speech throughout the day. But a Virtual Assistant is always listening for the wake word, meaning it can only start recording the speech after it hears it. It does not record anything spoken before the wake word. Also, every Virtual Assistant has an exit-intent activated if there is a long period of inactivity during an assistant conversation; the assistant will exit the conversation rather than keep listening.
• Security Concern: Virtual Assistants like Google Assistant only store user data with the user’s permission. These audio files, permitted by the user, are sent to the cloud and used by Google to improve the performance of Google Assistant. The voice commands provided by users are available to the Virtual Assistant providers in an unencrypted format. Hence there is a concern that the provider could share those commands with any unauthorized third party. Those voice commands may contain sensitive information about the user like Biometric Identity, personal details like passport number or phone number, location, medical history, and search patterns. Some voice commands can be directly embedded into music, advertisements, or other familiar sounds inaudible to the human ear. These embedded commands can wake the Virtual Assistant and perform specific malicious actions like sending messages, opening malicious websites, and even transferring money without the user noticing.

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Schools spent nearly $160 billion on education technology, or ed-tech, in 2016, according to EdTechXGlobal and Ibis Capital, and spending is expected to grow at a 17 percent annual rate through 2020. From 2011 to 2015, private investment in educational technology increased by 32% annually, reaching $4.5 billion globally.

The contribution of AI to these flows has not been calculated. Nonetheless, it is likely to rise as artificial intelligence technologies are well suited to achieving important educational goals such as improving teaching efficiency and effectiveness, providing education to all, and developing 21st-century skills.

So, in terms of artificial intelligence, where will education be in 2030? It will almost certainly play a significant role. On the other hand, success depends on technical and ethical issues, beginning with who owns student data, who can see it, who can use it, and for what purposes.

Bridging the skills gap

Many countries suffer from significant skill mismatches, which are caused by the education system’s inability to accurately reflect employer demands and labor market frictions that prevent individuals from being properly matched to jobs. Only half of the students in a survey of ten developed and developing countries believed their post-secondary education improved their employability. More than a third of employers cited skills shortages as a major reason for entry-level job openings. The resulting skills gap not only causes economic underperformance, but it also prevents many people from reaching their full potential.

Artificial intelligence will also play a key role in improving the connection between education and labor markets. By connecting talent with job opportunities, digital technologies are already making a difference. According to a recent MGI study, by 2025, online talent platforms could help up to 60 million people find work that better matches their skills or preferences while also lowering the cost of human resources management, including recruitment, by up to 7%. Artificial intelligence’s opportunities in employment-to-education settings have already begun to attract new players, thanks to a growing emphasis on lifelong learning.

Improved pattern recognition enabled by machine learning and detailed data on potential employees may help to improve recruitment results in the future. It can help hiring companies pinpoint the exact skill sets and personality traits that will enable someone to succeed in a job and uncover previously untapped insights in talent management. Artificial intelligence could also help recruiters avoid using school reputation as a proxy for evaluating candidates’ potential by detecting promising candidates with less traditional credentials. Fundamentally, artificial intelligence will improve education systems’ ability to meet the needs of future employers.

Attracting students and keeping them

Educators will be able to use personal, academic, and professional data and government data to ensure that students benefit from the courses they choose. The value is derived from students’ ability to excel academically and the institutions’ ability to assist them in finding meaningful employment. People who appear unsuitable based on traditional measures of academic success but have high potential based on other abilities and traits could be identified using machine learning. Students will benefit from better targeting because it will allow institutions to attract the right mix of people, improve learning outcomes, and help schools and universities improve their offerings over time.

Universities are already looking into AI applications to help students stay in school longer. Some colleges and universities are experimenting with advanced analytics and machine learning to identify students with difficulties and assist them before they drop out. Civitas Learning and Salesforce have teamed up to create a service for universities that identifies and engages students who are on the verge of dropping out. Machine learning is used by Salesforce tools to recommend engagement strategies that improve retention and graduation rates.

By monitoring students as they work, tracking their eye movements, and observing their expressions to see if they are engaged, confused, or bored, computer vision could detect signs of disengagement in the future. Some institutions in the United Kingdom are experimenting with computer vision, natural language processing, and deep learning algorithms to better understand students’ learning difficulties and preferences, incorporating novel data types such as students’ social media activities.

Unleashing personalized learning

Attracting and retaining students is critical, but the real educational breakthrough will most likely come from a fundamentally different approach to learning, whether in or out of the classroom. In recent decades, many efforts have been made to tailor learning to each student and move away from a standardized approach. Adaptive learning solutions seek to overcome the limitations of traditional classroom instruction by tailoring lesson plans to a student’s prior knowledge, learning preferences, and progress. Adaptive learning claims to deliver the right content, at the right time, in the best way to each student, rather than delivering a single lesson to the entire class, which can leave struggling students behind or disengage fast learners.

Artificial intelligence could improve adaptive learning and personalized teaching by identifying factors or indicators of successful learning for each student that was previously impossible to capture. In addition to tracking variables like the number of times a student pauses during a lesson, the amount of time it takes to answer a question, and the number of times a question is attempted before getting it right, computer vision and deep learning could pull in additional data like mouse movements, eye tracking, and sentiment analysis, providing deeper insights into a student’s performance, confidence, mindset, and cognitive ability.

AI-enabled adaptive learning could restructure education if implemented at scale. It could do away with traditional testing systems in favor of a more nuanced assessment of academic abilities and achievement. Teachers would focus less on lecturing and more on coaching, aided by prescriptive analytics to choose the most effective methods. Class formats would give students more room to learn according to their preferences, with teachers focusing less on lecturing and more on coaching.

Releasing teachers’ true value add

Teachers’ jobs may be stripped of time-consuming administrative tasks in the future, such as supervising and answering routine questions. Teachers would have more time to mentor and coach students, which are valuable tasks uniquely suited to humans.

Natural language, computer vision, and deep learning could help students with routine questions or as tutorial supervisors, allowing teachers to focus on other tasks. A virtual supervisor could use AI to track students’ work and behavior and provide teachers with statistically-based insights and constructive feedback on their progress. In the future, AI solutions may be able to monitor an entire classroom and call out students individually using voice and facial recognition.

Finally, by applying machine learning algorithms to data from students’ education profiles, social media, and surveys, AI in education could assist teachers in forming the most effective groups or classes. Companies like Collaboration.ai use artificial intelligence to process data on each student’s experience, knowledge, and capabilities, create instantaneous maps of connections and networks, highlight each student’s unique potential, break down preferences and bias, and recommend best-suited group formations for the learning objective. Complementary skills that maximize critical thinking and test students’ ability to adapt and collaborate can be identified using machine learning.

Toward virtual teachers

According to UNESCO, the world will need to recruit and train 24.4 million primary school teachers by 2030 to achieve universal primary education and 44.4 million secondary school teachers to fill openings by 2030. Many of these new hires—more than 85% in primary schools—will be required solely to replace teachers who leave the profession. Artificial intelligence may be a component of a solution. By supporting two key enablers of teaching: coaching, and assessing, AI-assisted teaching could significantly impact third-world countries and remote locations.

Coaching and assessing require specific skills that are currently beyond the capabilities of machines, such as emotional intelligence, creativity, and communication. Deep learning algorithms could recognize patterns, attitudes toward the learning situation, and affective states using new indicators such as facial expressions, digital interactions, group interactions, and attendance tracking and provide real-time support to students.

In addition to student assessment, AI-powered machines are making progress. Companies such as GradeScope already use computer vision and machine learning to grade students’ work faster than a teacher, starting with deciphering handwriting and remembering the teacher’s initial mark decisions to grade subsequent students automatically. Only work with objectively correct answers, such as math problems, and rule-based learning, such as orthography, languages, and historical events, can be evaluated with today’s technology.

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A virtual voice assistant is a software agent that can interpret human speech and respond via synthesized voices. It communicates with the users in natural language. The most popular voice assistants are Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant, incorporated in smartphones and dedicated home speakers.

Voice assistants use technologies like voice recognition, speech synthesis, and NLP to provide services to the users. Voice recognition is the heart of a voice application and is a rapidly evolving technology that provides an alternative to keyboard typing. Voice recognition is an important component for the user as a gateway to use his or her voice as an input component. It is expected to become the default input form for smartphones and cars, and other home appliances.

What can voice assistants do?

Some key elements distinguish voice assistants from ordinary programs. First, it has NLP, an ability to understand and process human languages by filling the gaps in communication between humans and machines. Second, it can use stored information and data and use it to draw new conclusions. Third, it is powered by machine learning that allows one to adapt to new things by identifying patterns.

Voice assistants have several interesting capabilities. They allow users to ask questions, control home automation devices, media playback and manage other basic tasks like email, to-do lists, and calendars with verbal commands.

There are a wide variety of services provided by the voice-enabled devices, ranging from simple commands like providing information about the weather of a place, general information from Wikipedia, movie rating from IMDB, setting the alarm or reminder, creating a to-do list, and adding items to the shopping list so that we don’t forget when we go shopping. Depending on the device provider or user preference, it can also read books for the user or play music from any streaming service. It can also play videos from YouTube or else from any streaming service.

In a recent study, voice assistants are also being used to assist public interactions with the Government, and a decrease of 30% work-load on humans when voice assistants are used in call centers. Although each currently available voice assistant has unique features, they share some similarities and can perform the following basic tasks:

• Answer to questions asked by users.
• Send and read text messages
• Make phone calls, and send and read email messages.
• Play music from streaming music services such as Amazon, Google Play, iTunes, Pandora, Netflix, and Spotify.
• Set reminders, timers, alarms, and calendar entries
• Make lists, and do basic math calculations.
• Play games.
• Make purchases.
• Provide information about the weather.
• Control Internet-of-Things-enabled smart devices such as thermostats, lights, locks, vacuum cleaners, switches).

Limitations

While voice assistants have interesting and useful features, they also pose several unique problems. One main issue with these voice-activated devices is security. Anyone with access to a voice-activated device can ask it questions, gather information about the accounts and services associated with the device. This poses a major security risk since the devices will read out calendar contents, emails, and highly personal information.

Voice assistants are also vulnerable to several other attacks. Researchers have recently proven that voice assistants will respond to inaudible commands delivered at ultrasonic frequencies. This would allow an attacker to approach a victim, play the ultrasonic command, and the victim’s device would respond.

Privacy is another big concern for voice assistant users. By their very nature, these devices must be listening at all times to respond to users. Amazon, Apple, Google, and Microsoft insist that their devices are not recording unless users speak the command to wake the assistant. Still, there has been at least one case where a malfunctioning device was recording at all times and sending those recordings back to Google’s servers. Even if the companies developing these voice assistants are being careful and conscientious, there is a potential for data to be stolen, leaked, or used to incriminate people.

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With these learnings in place, healthcare professionals are already having conversations on how to realign their ambitions, goals, and strategies to address these pain points as we plan how to emerge from the pandemic and ultimately provide the best patient outcomes across every category in healthcare.

In 2021, uncertainty still looms, but one thing is certain, the healthcare system will continue to be transformed by technology. We’ll see more providers adopt technologies that bring smart automation to patient care, along with A.I. and machine learning to better analyze patient data in order to drive better informed business decisions in healthcare. Hospitals and healthcare facilities can most easily automate for improved care through the integration of robotic technologies and artificial intelligence.

Assistive robotic technologies for in-patient care facilities and homes

Robotics have emerged in every industry, and leaning on this assistive technology to provide support, precision, and speed provides countless benefits to those who integrate the technology. Looking at the recent news of Hyundai’s purchasing a controlling stake in a deal that values Boston Dynamics at $1.1 Billion, to automate its own mobility business, has provided a bellwether moment for the robotics industry as a whole. These assistive technologies bring the automation and precision to drive better business outcomes, and it’s true for the healthcare industry and its patients as well.

By 2021, the demand for healthcare robotics is expected to rise to $2.8 billion in sales. That’s because robot applications in healthcare seem endless. It’s a market that involves robotics for surgery, recovery, and clinics. What’s more is that the American Physical Therapy Association (APTA) referred to the pairing of robotics and physical therapy specifically as “the new age of function, movement, and recovery.” Robots are now being used to help patients to regain mobility and to guide them through therapy sessions to improve strength, range of motion and especially coordination. Coming out of the pandemic, we can expect robotics to take on a larger role in patients, both in medical facilities and at home for ongoing care

AI and data will drive positive patient outcomes

The pandemic has allowed telemedicine to emerge as a fundamental tool for clinicians to continue their work on diagnosing and treating patients remotely. With this approach to healthcare delivery, remote patient monitoring has opened the door to collect and analyze this data, allowing for better decision making by medical professionals.

With the availability of secure cloud-based systems providing real time data, accessible anytime remotely, medical professionals can now rely on these stats to inform their decision-making process in offering a diagnosis or treatment plan. From wearable devices to implanted medical devices, and home therapy devices we now have access to information that enables us to measure and connect data that we have not been able to analyze in the past. With access to patient information, clinicians can rally patients for success by sharing customizable reports and involving family members in the treatment and recovery process along the way. These enhanced patient’s motivation and support of their beloved one’s matter to recover.

Internally, patient data from patient monitoring systems, wearables, and other assistive technologies can better allow hospital staff to analyze and communicate on patients’ needs while improving the overall adoption of technology. A.I. and machine learning can take the complexity of the data and make it more accessible to hospital staff who may be reluctant to acclimate to new technologies. With better adoption of these technologies, hospital management teams can continue to advance their care and ultimately what’s best for each individual patient.

Robotics and data will continue to play a larger role in driving healthcare delivery in 2021, with its ease of use for clinicians to automate care with precision, while A.I. will lean on newly available patient data to provide predictive care and personalization. The growing adoption of robotics, machine learning and artificial intelligence will provide improved results in patient access and outcomes.

About the Author:

Dr. Eric Dusseux, is Chief Executive Officer, BIONIK Laboratories, a robotics company focused on providing rehabilitation and mobility solutions to individuals with neurological and mobility challenges from hospital to home. The Company has a portfolio of products focused on mobility and upper extremity rehabilitation for stroke and other mobility-impaired patients, including three products on the market and two products in varying stages of development.

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Banks with a huge customer base are looking at chatbot as a smart, self-service, 24/7 customer service channel that can handle a large number of customer inquiries and evolving banking needs of customers without placing too much pressure on their customer service agents.

Chatbots are fast, easy-to-use, and can address multiple customers at a time. They use artificial intelligence to mimic human interactions through a chat interface, allowing customers to obtain the information they want using simple, natural conversational language.

Embedded in the customer service through major messaging applications, they enable personalized services, reduction in waiting time for users, uninterrupted customer support, and a feedback channel to a large number of customers, and guaranteeing consumer satisfaction. With chatbots, it is possible to increase efficiencies up to 80% and automate the majority of all incoming queries.

In this post, we will look at some of the top chatbots and virtual assistants launched by leading banks in India.

1. SBI Intelligent Assistant (SIA)

India’s largest public-sector lender State Bank of India (SBI) deployed its AI-based financial chatbot named SBI Intelligent Assistant (SIA) in 2017 with a capability to respond to 864 million queries a day, related to home, education, car and personal loans along with recurring and term deposits and frequently asked questions like ATM locations and IFSC codes.

SIA can handle nearly 10,000 inquiries per second, and Google processes almost 25% of the queries. This multilingual chatbot, which can respond in 14 languages in speech or text, was developed by Payjo, a Silicon Valley-based company with an operation center in Bengaluru. Since the launch, the bank saw a significant reduction in operational expenditure over time.

2. HDFC Bank’s EVA

HDFC Bank’s EVA (Electronic Virtual Assistant) is India’s first and largest Artificial Intelligence-powered banking chatbot, built to leverage the latest technologies to help serve customers better and faster. Launched in 2017, Eva has already answered more than 5 million queries from around a million customers, with more than 85% accuracy. Customers can get the information instantaneously by conversing with Eva, instead of searching, browsing, clicking buttons, or waiting on a call.

Eva can hold more than 20,000 conversations every day with customers from all over the world. Eva uses AI and Natural Language Processing to understand the user query and fetch the relevant information from thousands of possible sources, all in a matter of milliseconds. Eva was built and managed by Senseforth AI Research Private Limited, a leading AI startup working on cutting-edge research in conversational banking.

3. ICICI BANK’s iPal

ICICI Bank deployed its AI-powered chatbot iPal in 2017. In just eight months of its launch, the chatbot has interacted with close to 3.1 million customers, addressing 6 million queries with nearly 90% accuracy. According to sources, the chatbot handles 1 million chats per month. It offers an instant resolution to all customer queries on the website and mobile banking application, iMobile, which is used by 6 million customers.

It also enables customers to undertake financial transactions like bill pay, fund transfer, and recharges. Built with a partnership between the internal bank team, a fintech firm, and an international tech firm, the chatbot supports all vernacular languages, voice support, and API integration with platforms like the Google Assistant, Siri, Facebook messenger.

4. YES ROBOT

India’s fourth-largest private sector bank, Yes Bank launched its AI-enabled chatbot, YES ROBOT in 2018, with advanced NLP engine LUIS (Language Understanding Intelligent Service) and other cognitive services, capable of understanding and resolving the banking needs of customers without human intervention. The chatbot can handle around half a million customer interactions every month.

The bank is partnering with Microsoft to strengthen its chatbot with an advanced natural language processing engine called LUIS (Language Understanding Intelligent Service) and other cognitive services. YES ROBOT enables the customers to perform financial and non-financial transactions in simple conversations without the hassle of navigating through multiple web pages. The chatbot allows customers to comprehensively manage their Credit Card, view summary, bill payment, reward points, and international card usage.

One of the most key features of this chatbot is the option to book fixed deposits (FDs) and recurring deposits (RDs) by merely conversing with it, without registration or passwords (only OTP is required). The bot deposited worth Rs. 5.2 billion booked through YES ROBOT in the first year of its launch. Even with typos and human errors, the chatbot can identify the user’s intent with over 90% accuracy.

5. IndusAssist

IndusInd Bank’s AI chatbot IndusAssist was launched in 2018 in partnership with Amazon’s Alexa in order to enable the customer to avail banking services by merely talking to Alexa. Customers can perform financial and non-financial banking transactions on Amazon Echo, and other Alexa enabled devices using voice-based commands using the chatbot.

To use the service, customers need to do a one-time registration to link their bank details using the Alexa app on their smartphone. Post-registration, all authentication, and transaction requests will remain voice-based. The transactions would follow the standard two-step authentication process to ensure that they are safe and secure.

With the bot, the customers will be able to recharge their mobile phones, pay credit card bills, and so on by voicing out simple commandments, such as ‘Alexa, ask IndusAssist to recharge my mobile number’, or ‘Alexa, ask IndusAssist to pay my credit card bill’.

6. Kotak Bank’s Keya

Kotak Mahindra Bank’s AI-driven conversational voice bot Keya was launched in 2019. Keya is quick to answer banking queries round the clock and can field questions on credit cards, debit cards, savings and current accounts, 811 accounts, fixed deposits, and fund transfers. This bilingual bot, available in English and Hindi, uses automatic speech recognition, natural language understanding, and text-to-speech technology to help customers navigate through the IVR.

Keya understands the caller’s intent, verifies it, and then offers relevant solutions, which can result in greater call routing accuracy, reduced call duration, and improved customer satisfaction. Keya has crossed over 3.5 million queries from over 1 million unique users, with 93% accuracy.

7. Axis Aha

Axis Bank launched Axis Aha in early 2018. This virtual banking assistant, built in partnership with Singapore based tech firm Active.Ai, brings the power of AI and machine learning to help customers with contextual conversations, do transactions, and answer their banking related queries.

It is capable of transferring funds, ordering a cheque book, clearing credit card and utility bills, enhancing debit card limits, and switching off debit card temporarily. Powered by Active.Ai’s AI engine TRINITI, the bot can understand customers’ intent, be contextually aware, handle multiple instructions in a single string, acronyms, or slangs.

8. Andhra Bank’s ABHi

Andhra Bank launched its AI-based interactive assistant ABHi in 2019. ABHi uses the latest AI and NLP algorithms to understand the customer query and fetch the relevant information from its knowledge base in milliseconds. The customers will be able to get the information instantaneously 24×7, anytime they want. They can connect and know details from ABHi through the bank’s website over mobile/desktop browser, Facebook Messenger, and over voice using Google Assistant. They can ask details on digital banking, loans, banking services, government schemes, insurance, etc., lodge complaints, know the nearest branch / ATM on Google Map and recharge prepaid mobile.

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