Insights
AI Agents: Redefining Productivity and Decision-Making Across Industries
Artificial Intelligence has significantly transformed business dynamics over the past decade. From the increased presence of Conversational AI in Customer Interaction Management systems to the possibility of using intelligent forecasting and predictive models to inform business strategies, AI has rapidly become an essential asset for businesses that want to stay competitive across all industries. One of the richest, most interesting and promising applications of AI in business are AI Agents. In a nutshell, an AI Agent is a software program or system designed to perform tasks autonomously, using artificial intelligence techniques. Agents are already ubiquitous, spanning from sophisticated driver-assistance systems to intelligent speakers capable of compiling to-do lists or delivering up-to-the-minute updates on weather and traffic conditions.
AI agents are set to bring about a new era of smart automation, changing industries and helping humans be more productive and innovative. The number of AI Agents created and deployed by businesses grew by 119% in the first half of 2025, with sales and service emerging as the top agentic use cases. (Salesforce, 2025). AI Agents are often capable of not only exceptionally detailed and fine-grained AI Analytics, but also of making autonomous, intelligent decisions based on them.
However, there are many questions about how AI Agents will develop and what exactly they will mean for how companies conduct business. What applications will we be seeing in the future for AI Agents? What industries can benefit the most from AI Agent technology? How will the advent of the AI Agent affect human specialists?
In this article, we’ll try to address all these questions. But first, let’s start with a definition: what exactly is an AI Agent?
What Is an AI Agent and How It Redefines Automation and Adaptability
An AI Agent is a specialized system that leverages Agentic AI to solve problems in an intelligent, goal-directed manner, requiring only a clearly defined objective—ranging from tasks like financial analysis to planning complex trips. Once given a goal, these agents autonomously generate and organize their own sub-tasks, continuously adapting and evolving their strategies to achieve desired outcomes. In essence, they create their own prompts and decision pathways as they progress, operating with a degree of independence far beyond traditional automation. Unlike conventional automated systems, which rely on predefined triggers or rigid workflows, AI agents excel in dynamic, uncertain environments, analyzing new information in real-time and selecting appropriate actions based on context.
AI agents find applications across a broad spectrum of domains, including robotics, gaming, virtual assistants, and autonomous vehicles. Their operation can be reactive, responding immediately to changes; deliberative, planning and reasoning before acting; or learning-based, improving performance over time by drawing on experience.
In contrast, interacting with standard AI models typically involves a human-in-the-loop process: a user provides a prompt, receives a response, and then formulates the next prompt based on the output. AI agents function differently. They operate autonomously, perceiving their environment through sensors, processing information with algorithms or internal models, and acting via actuators to influence their surroundings. This autonomy allows them to continuously pursue objectives without requiring constant human intervention, effectively bridging the gap between reactive tools and intelligent, self-directed problem solvers.
How AI Agents Work
Agent Function
The core of an AI Agent is its agent function, which determines how it converts gathered data into actions. This function represents the agent’s “intelligence,” driving its decision-making to accomplish set objectives.
Percepts
Percepts are the sensory inputs an AI agent receives from its environment, providing information about the current state of its surroundings. For example, in a customer service chatbot, percepts might include user messages, profile details, location, chat history, preferences, and emotion detection.
Actuators
Actuators serve as the “muscles” of the agent, carrying out the decisions made by the agent function. These actions can include steering a self-driving car or generating text responses in a chatbot. Typical actuators include text generators, service integration APIs for accessing external systems, and notification systems for alerting users.
Knowledge Base
The knowledge base is the repository of the agent’s initial understanding of its environment, whether predefined or acquired during training. It underpins the agent’s decision-making, storing information like traffic laws for a self-driving car or detailed product information for a customer service agent.
Summary
AI agents differ from simple AI models by functioning autonomously toward specified objectives rather than relying on human-generated prompts for each action. They generate and adapt tasks dynamically, perceiving their environment through sensors, processing information with algorithms or models, and acting via actuators. Their intelligence is embodied in the agent function, which converts percepts—sensory inputs from the environment—into actions. Actuators execute these decisions, such as driving a car or responding in a chatbot, while the knowledge base provides foundational information for decision-making. AI agents range from simple rule-based systems to sophisticated, learning-capable entities and are applied in fields like robotics, gaming, virtual assistants, and autonomous vehicles, excelling in dynamic, information-rich environments.
Different Kinds of AI Agents and What They’re Good At
Simple Reflex Agents
These agents operate according to a predefined set of condition-action rules and respond only to the current percept, without considering past inputs. They are most effective in handling tasks that are simple and have a limited scope.
Model-Based Reflex Agents
Using a more advanced approach, model-based agents keep an internal representation of the environment to guide their decision-making. This ability allows them to handle more complex tasks effectively.
Utility-Based Agents
These agents evaluate the expected utility of each possible action to make decisions, which is especially valuable in situations where comparing different options is crucial for choosing the best course of action.
Learning Agents
Learning agents are designed to operate in unfamiliar environments and adapt their actions based on experiences. They use techniques such as deep learning and neural networks for ongoing improvement.
Belief-Desire-Intention Agents
These agents emulate human-like behavior by holding beliefs about the environment, desires, and intentions. They can reason and plan their actions based on these factors, making them ideal for managing complex systems.
AI Agents in Customer Service: Autonomous Support for the Modern Business
While, as we have seen, AI agents are prevalent in various sectors their impact on customer service is particularly noteworthy. AI agents have revolutionized the business landscape, particularly in customer service, by fundamentally changing how companies engage with their customers. As part of AI Customer Experience solutions, they have become a cornerstone technology, enabling businesses to automate tasks, provide personalized support, and deliver more meaningful interactions.
In this section, we'll examine the significance of AI Agents in the Customer Experience and Customer Service sectors, exploring their advantages over Generative AI models and traditional chatbots and considering some of their use cases.
What are Customer Service AI Agents?
In other articles, we have covered some of the most important ways Customer Service AI tools have impacted the customer engagement landscape. Agentic AI stand to revolutionise customer service AI, emerging as one of the most impactful and groundbreaking applications of artificial intelligence. Over the past year, it has delivered unprecedented performance, transforming AI Automation into a more powerful, multi-dimensional tool for performance and productivity far surpassing traditional Generative AI solutions, enabling companies to provide faster, smarter, and more personalized support than ever before.
At their core, as we have seen, AI agents are software programs designed to perform tasks autonomously using advanced AI techniques. While their applications span numerous industries, their impact on customer service is particularly remarkable. By understanding context, generating solutions, and executing actions independently, AI agents provide a level of responsiveness and personalization that goes far beyond conventional automation, setting a new standard for how businesses interact with their customers.
Agentic AI systems can act autonomously to complete tasks, make decisions, and interact with customers with minimal human oversight. Unlike traditional AI chatbots, which mainly respond to queries based on scripts or data retrieval, agentic AI can proactively solve problems and optimize customer interactions.
What are the differences between Customer Service AI Agents and Chatbots?
Unlike traditional AI models that require manual prompts to generate responses, AI agents operate autonomously. In customer service, this means that once an objective is set—such as resolving a customer issue or processing a request—the AI agent independently develops a strategy and executes it. This ability to act without constant human intervention allows AI agents to handle customer interactions more efficiently and effectively.
Whereas conventional automation relies on predetermined rules and triggers, AI agents excel in navigating the unpredictable, dynamic environment of customer service. They continuously adapt to new information, ensuring that they deliver timely and relevant support. Modern AI customer service chatbots have evolved from basic scripted systems into advanced, generative AI-driven agents. These AI Agents handle complex conversations, integrate with backend systems, and continuously improve, resolving over 80% of queries autonomously while escalating complex issues to human agents as needed.
Feature | Rules-Based AI (Traditional Chatbots) | Generative AI (Conversational AI) | Agentic AI (Autonomous AI Agents) |
|---|---|---|---|
Primary Function | Matches input to predefined responses | Generates responses using language models | Acts autonomously to achieve goals through reasoning |
Technology Core | Rule engines and decision trees | Large Language Models | LLMs + planning + tool integration |
Workflow Structure | Static, predefined | Flexible, conversational | Dynamic, adaptive, multi-step |
Autonomy | None (follows scripts) | Low (reactive responses) | High (proactive, self-directed) |
Context Awareness | Limited to current input | Within conversation | Across sessions and systems |
Memory | None | Short-term (session only) | Persistent long-term |
Reasoning Capability | None or basic logic | Some implicit reasoning | Multi-step reasoning and decision-making |
System Integration | Pre-programmed API calls | Limited (information retrieval) | Full: APIs, CRMs, databases |
Personalisation | Static and rule-based | Textual personalisation | Behavioural and contextual personalisation |
Best For | Simple FAQs, basic routing | Content generation, Q&A | Complex workflows, sales automation |
How can modern businesses leverage Agentic AI for Customer Service?
Agentic AI is reshaping customer service software, empowering teams to scale effortlessly, expand their reach, and operate with unprecedented efficiency. As a true breakthrough in the enterprise tech stack, AI agents push Customer Experience Automation further—tackling complex challenges with intelligence and autonomy.
The impact is already evident: over the past year, companies and customers have seen AI models dramatically improve in their ability to resolve queries and deliver effective, human-like support. According to recent research by Zendesk, 70% of CX leaders believe Conversational AI models are becoming skilled architects of highly personalized customer journeys. Other studies show that, in 2025, 50% of customers have successfully resolved a service issue using AI without human assistance—up from 32% in 2024, marking a 51% increase.
Other studies by Salesforce show that, in 2025, 94% of consumers opted to interact with AI when given the choice, and a related global survey found that nearly 60% of regular users believe AI has become noticeably more helpful over the past year. The same study shows that, compared with consumers who rarely use AI agents, regular users report dramatically better experiences: 46% higher satisfaction, a 122% greater likelihood of saying AI services have become more helpful in the past year, a 115% greater likelihood of seeing them as more intelligent, and a 229% greater likelihood of viewing them as more proactive.
This shift is being accelerated by the rapid, widespread adoption of Agentic AI, which marks a significant evolution beyond earlier forms of AI such as generative models. In the first half of 2025, customer service conversations with AI agents grew by a six-month compound annual growth rate (CAGR) of 2,199% for the average business (Salesforce, 2025). A 2025 study by Gartner estimates that, by 2029, agentic AI will autonomously resolve 80% of routine customer service issues.
But how are companies using AI Agents for customer service? Let's see it in the next section.
How can modern businesses leverage Agentic AI for Customer Service?
AI agents have significantly transformed customer service across various industries by delivering efficient and personalised support. Leveraging advanced natural language processing (NLP) and machine learning algorithms, these agents—often deployed as chatbots or virtual assistants—interact with customers in real-time. They handle a wide range of tasks, from answering questions and providing information to resolving issues promptly. This capability allows businesses to offer continuous, 24/7 assistance, which enhances customer satisfaction and loyalty while reducing the reliance on human agents for routine queries.
AI agents do more than streamline customer interactions; they also provide valuable insights into customer behavior thanks to Customer Interaction Analytics features. By analysing data from customer queries, preferences, and feedback with advanced AI Analytics features, these agents help businesses gain a deeper understanding of their customers’ needs, uncovering trends and guiding product or service improvements.
In addition to these insights, AI agents with coaching functions assist customer service representatives during conversations. They offer real-time guidance and suggestions to improve interaction quality and efficiency. AI Analytics further enhance business operations by providing actionable intelligence, optimising customer service strategies, resource allocation, and overall customer experience. This comprehensive support helps businesses achieve greater success and maintain a competitive edge in the marketplace.
Autonomous Issue Resolution. AI Agents can identify the root cause of customer problems through analysis of past tickets, transaction history, or product data, executing actions on behalf of the customer, such as processing refunds, upgrading subscriptions, or initiating returns, escalating only when human intervention is truly necessary.
Proactive Customer Engagement. Agentic AI can predict customer needs based on behavior patterns and reaching out proactively and suggesting personalized solutions or products before the customer asks for them.
Dynamic Interaction Management. Agentic AI can understand complex, multi-turn conversations without relying on scripted flows, adjust tone, language, and approach based on customer sentiment and engagement style, and handle simultaneous tasks, such as checking order status while recommending alternatives.
Learning and Improvement. AI Agents continuously analyse interactions to improve response strategies, detects recurring issues and proposes systemic solutions to reduce future service load, and adapts to new products, policies, or customer behaviors without manual reprogramming.
Data-Driven Insights. Agentic AI can summarise customer interactions and sentiment for management, identify trends in complaints, product issues, or opportunities for upselling and cross-selling, and inform strategic decisions by integrating customer feedback directly into business processes.
Real-Time Coaching. Agentic AI can provide real-time AI coaching by monitoring customer interactions and offering agents discreet, actionable guidance—such as suggested responses, tone adjustments, or next-step recommendations—to improve performance and enhance the customer experience.
The future of AI Agents
The future of AI agents in customer service holds vast potential, from further enhancing personalisation to integrating more deeply with other business systems to provide a seamless customer experience. However, this future also brings challenges, such as ensuring ethical use and maintaining the balance between customer service automation and human touch.
Despite concerns about job displacement, AI agents are more likely to augment human roles rather than replace them. By taking over routine tasks, AI agents free up human agents to focus on more complex and emotionally nuanced customer interactions. This shift not only improves efficiency but also leads to higher job satisfaction as human agents can engage in more meaningful work.
To fully realise the benefits of AI agents, companies need to invest in the right technology, provide training for their workforce, and continually assess and refine their AI-driven strategies. As AI agents continue to evolve, they will undoubtedly become an even more integral part of customer service, driving innovation, improving customer satisfaction, and maintaining competitive advantage in a digital-first world.
Summary
AI agents have revolutionised customer service by enabling businesses to interact with customers more efficiently, personally, and continuously. Operating autonomously with advanced AI techniques like natural language processing and machine learning, they handle complex queries, adapt to dynamic situations, and integrate with backend systems. Beyond resolving routine issues, they provide insights through customer analytics and support human agents in real time, freeing them to focus on high-value interactions. By improving efficiency, customer experience, and job satisfaction, AI agents are becoming central to innovation and competitive advantage in today’s digital landscape.
Feature | Rules-Based AI (Traditional Chatbots) | Generative AI (Conversational AI) | Agentic AI (Autonomous AI Agents) |
|---|---|---|---|
Primary Function | Matches input to predefined responses | Generates responses using language models | Acts autonomously to achieve goals through reasoning |
Technology Core | Rule engines and decision trees | Large Language Models | LLMs + planning + tool integration |
Workflow Structure | Static, predefined | Flexible, conversational | Dynamic, adaptive, multi-step |
Autonomy | None (follows scripts) | Low (reactive responses) | High (proactive, self-directed) |
Context Awareness | Limited to current input | Within conversation | Across sessions and systems |
Memory | None | Short-term (session only) | Persistent long-term |
Reasoning Capability | None or basic logic | Some implicit reasoning | Multi-step reasoning and decision-making |
System Integration | Pre-programmed API calls | Limited (information retrieval) | Full: APIs, CRMs, databases |
Personalisation | Static and rule-based | Textual personalisation | Behavioural and contextual personalisation |
Best For | Simple FAQs, basic routing | Content generation, Q&A | Complex workflows, sales automation |
