AI Agents Explained: Transforming Industries and Workflows

An artificial intelligence (AI) agent is a sophisticated system or program that excels at autonomously performing tasks on behalf of a user or another system. It designs its workflow, harnesses available tools, and confidently carries out its mission with precision.

AI agents offer a diverse range of functionalities that go well beyond natural language processing. They are adept at decision-making, solving intricate problems, interacting with their environment, and executing actions—each step imbued with proactive intelligence.

Deployed across numerous contexts, these agents drive innovation in enterprise by solving complex challenges—from software design and IT automation to code-generation tools and conversational assistants. They utilize the advanced natural language techniques of large language models (LLMs) to interpret, execute, and refine user inputs step-by-step while determining when to access external tools.

How are AI agents used in industry?

In today’s dynamic industrial landscape, AI agents are instrumental in enhancing workflows, personalizing customer interactions, and forecasting trends with predictive analytics. Their seamless integration and highly skilled machine learning capabilities empower businesses to remain agile, continuously innovate, and drive efficiency.

What impact do AI agents have on customer service roles?

As of 15 August 2025, AI agents have confidently filled 43% of customer service roles. By harnessing automation and advanced data analytics, they elevate service delivery, tailor customer experiences, and free up human talent to focus on more strategic challenges.

How do AI agents personalise experiences?

AI agents skillfully blend data, machine learning, and predictive analytics to craft highly personalized interactions and services. This fusion of cognitive computing and intelligent automation enriches the customer journey, promising experiences that resonate on an individual level with precision and insight.

Why are AI agents considered innovative?

AI agents represent the pinnacle of technological innovation. Their ability to learn via neural networks, adapt fluidly, and execute tasks independently using state-of-the-art algorithms breaks new ground in all domains. The unique blend of data analytics and machine learning unlocks unprecedented pathways for creativity and progress.

What can we expect from AI agents in the future?

Looking ahead, AI agents are poised to continually evolve with enhanced automation, deeper data analytics, and refined cognitive computing capabilities. They will forge smarter solutions and drive transformative progress across industries, enriching both professional and societal landscapes.

Imagine a bustling café in Melbourne where a barista, supported by a dedicated AI agent, completes orders with unparalleled efficiency—streamlining processes with automation and machine learning, and revolutionizing the hospitality experience.

This AI agent not only processes orders but also personalizes customer experiences through advanced neural networks and data analytics, heralding a new era for the hospitality industry.

On 15 August 2025, AI agents managed 43% of customer service roles, illustrating the potent role of automation and cognitive computing in modern service delivery.

AI agents are reshaping industries by enhancing efficiency through predictive analytics and machine learning, and in doing so, they are actively transforming our daily professional lives.

AI agents are revolutionizing industries with their exceptional ability to process data, learn dynamically from it using neural networks, and make autonomous decisions driven by automation and cognitive computing.

These groundbreaking technologies are ushering in an era of limitless possibilities, powered by the most advanced machine learning and state-of-the-art data analytics.

Envision a future where AI agents effortlessly optimize your daily tasks—they enhance operations with highly skilled automation, deliver personalized experiences via predictive analytics, and even forecast future trends with remarkable precision.

AI agents stand at the forefront of innovation, poised to reshape our world with advances in machine learning, automation, and cognitive computing.

By harnessing state-of-the-art algorithms and deep data analytics, these digital pioneers are set to unlock significant advancements that promise enhanced efficiency, effectiveness, and remarkable user engagement across a range of applications.

Integrating AI agents into our lives means embracing a future brimming with opportunities—a future driven by the promise of automation, neural networks, and deep learning technologies.

As these AI agents continue to mature, they will redefine industry standards through improved data analytics and machine learning, spark creative solutions via cognitive computing, and empower communities.

With every breakthrough comes the potential for smarter solutions—rooted in automation and predictive analytics—that forge stronger, more sustainable outcomes.

Our journey with AI agents is not merely about building machines; it is about creating a tomorrow rich with potential and progress, driven by the brilliance of advanced machine learning and data analytics.

Embracing AI agents ignites a transformative journey, paving the way for limitless innovation and extraordinary progress across industries—powered by automation, neural networks, and cognitive computing.

Across every corner of our world, AI agents are steadily reshaping industries and enhancing daily lives with the power of predictive analytics, deep learning, and highly skilled data analytics.

Their impressive ability to elevate efficiency and customize experiences positions them at the heart of evolving business and societal norms, ushering in a groundbreaking era of advanced machine learning and automation.

How AI agents work

At the core of AI agents lie large language models (LLMs). For this reason, these agents are often referred to as LLM agents. While traditional LLMs like IBM® Granite™ models generate responses based solely on training data and are limited in reasoning, agentic technology uses powerful tool-calling capabilities on the backend. This enables AI agents to retrieve up-to-date information, optimize workflows, and autonomously decompose complex goals into manageable subtasks.

In this innovative process, the autonomous agent adapts to user expectations over time. By storing past interactions in its memory and planning future actions, the agent delivers personalized and comprehensive responses. The remarkable tool-calling mechanism operates independently of human intervention, vastly expanding the real-world applications of these AI systems. The journey to achieve a user-set goal unfolds in three dynamic stages:

Goal initialization and planning

Even though AI agents operate autonomously, they start with clear goals and defined environments provided by humans. Three key influences shape the behavior of an autonomous agent:

The team of developers who design and train the agentic AI system.
The team that deploys the agent and grants the user access.
The user who sets the specific goals and defines the available tools.

With these defined goals and tools, the AI agent embarks on task decomposition to optimize performance. In essence, the agent crafts a detailed plan comprising specific tasks and subtasks to achieve the complex goal.

For simple tasks, elaborate planning may not be necessary, as the agent can iteratively evaluate and refine its responses without pre-planning each step.

Reasoning using available tools

AI agents base their actions on the information they perceive. At times, an agent’s intrinsic knowledge may be incomplete for addressing every subtask within a complex goal. To bridge these gaps, AI agents adeptly use available tools such as external data sets, web searches, APIs, and even other agents. After obtaining the necessary information, the agent updates its knowledge base, continuously reassessing and refining its plan.

Consider a user planning a vacation. The AI agent is tasked with predicting the most favorable week for a surfing trip in Greece. Since the core LLM is not specialized in weather forecasting, the agent accesses an external database containing historical daily weather reports for Greece.

Despite gathering this vital information, the agent still needs to pinpoint the precise conditions ideal for surfing. To solve this, the agent reaches out to an external expert agent specializing in surfing. Through this collaboration, it learns that high tides, sunny weather, and minimal rain create the perfect setting for surfing.

Merging insights from its various tools, the agent confidently predicts the week in the upcoming year most likely to offer ideal conditions—high tides, clear skies, and low chances of rain. The combined power of exchanging information between tools enables AI agents to excel far beyond the capabilities of traditional models.

Learning and reflection

AI agents harness feedback mechanisms—whether from fellow AI agents or from human-in-the-loop (HITL) supervision—to refine the accuracy of their responses. Reflecting on our previous surfing example, after the agent presents its recommendation, it remembers the learned insights along with the user’s feedback to improve future performance and better align with evolving user expectations.

If additional agents contributed towards achieving the goal, their feedback is similarly integrated. Multi-agent feedback accelerates the process, reducing the time users need to supply guidance. Meanwhile, continuous user feedback during the agent’s actions and internal reasoning further hones the outcome.

These feedback mechanisms, often termed iterative refinement, significantly boost the agent’s reasoning and accuracy. To prevent repeated mistakes, AI agents can store successful solutions and lessons learned in their ever-growing knowledge base.

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Agentic versus non-agentic AI chatbots

AI chatbots employ conversational AI techniques such as natural language processing (NLP) to understand user inquiries and automate responses. They represent one modality; however, agency is a broader technological framework.

Non-agentic AI chatbots, lacking access to external tools, memory, and deep reasoning, can only pursue short-term goals and cannot plan ahead. They depend continuously on user input to generate responses. While they perform well with common prompts, their ability to address unique user-specific concerns is limited by their inability to learn from past interactions.

In contrast, agentic AI chatbots evolve by adapting to user expectations over time. They deliver personalized experiences and comprehensive solutions by autonomously generating subtasks and revising their plans as needed. Unlike their non-agentic counterparts, these smart agents assess available tools and bridge information gaps to produce exceptional outcomes.

Reasoning paradigms

There is no single architectural standard for building AI agents; instead, several paradigms exist to address multi-step challenges with clarity and precision.

React (Reasoning and Action)

This paradigm inspires agents to “think” and plan after every action and tool response, meticulously deciding which tool to invoke next. These Think-Act-Observe loops guide the agent as it resolves problems step by step and continuously refines its responses.

Through its structured prompt, the agent is encouraged to reason deliberately and reveal each “thought.” This verbal reasoning not only provides transparency but also illustrates how responses are formulated—a practice reminiscent of Chain-of-Thought prompting.

ReWOO (Reasoning WithOut Observation)

The ReWOO method departs from reliance on immediate tool outputs for planning. Instead, agents confidently plan their actions upfront, strategically reducing redundant tool usage by anticipating which resources will be needed when the initial prompt is received. This forward-thinking approach allows users to review and confirm the plan before execution.

The ReWOO workflow consists of three dynamic modules. First, the planning module forecasts subsequent steps in response to the user’s prompt. Next, the agent collects outputs from the selected tools. Finally, it seamlessly integrates the original plan with the tool outputs to form a coherent response. This pre-planning approach significantly lessens token usage and computational complexity, while mitigating the effects of intermediate tool failures.

Types of AI agents

AI agents are crafted with diverse capabilities tailored to different challenges. From the simplest to the most advanced, five main types of agents illustrate the evolution of technology:

1. Simple reflex agents

Simple reflex agents represent the most foundational form, executing actions based solely on current perceptions. Without memory or the ability to interact with other agents for additional information, these agents operate on preprogrammed rules—simple, yet effective when conditions are perfectly observable.

Example: A thermostat that activates heating at a predetermined time every night. The rule is simple: if it is 8 PM, then activate the heating system.

Simple reflex agent diagram

2. Model-based reflex agents

Model-based reflex agents elevate the concept by combining current perceptions with memory to maintain an internal model of the world. As fresh information is received, this internal model is continuously updated. The agent’s actions are guided by this enriched model, its preset reflexes, previous perceptions, and the current state—all contributing to more informed decision-making in evolving environments.

Example: A robot vacuum cleaner that not only detects obstacles like furniture as it cleans but also remembers the areas it.

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