AI Agents are emerging as the next significant evolution in Artificial Intelligence (AI). Like Generative AI (GenAI), they are part of the vast and complex AI ecosystem. However, to recognize their true impact, we must consider them within the broader context of AI’s rapid advancements – particularly in Conversational AI, where they serve as a logical next step. By enabling more autonomous and intelligent interactions, AI Agents have the potential to revolutionize customer service.

In this article, the BOTSchool team delves into the concept of AI Agents in depth, examining what they are, the technology behind them, various viewpoints on their definition, and whether they hold the key to the future of enterprise AI.

Agentic AI (the concept behind AI Agents) may be unclear to those unfamiliar with the tech landscape. At first glance, the term “Agentic” might suggest human agents performing tasks like customer service. However, its meaning goes far beyond that. To fully understand this emerging technology, we need to break it down and explore what Agentic AI represents.

Agentic AI is an advanced approach to AI that enables the creation of autonomous agents – intelligent systems capable of operating without human supervision. Unlike traditional AI models, which follow predefined instructions, Agentic AI-driven agents have greater flexibility and can make decisions, learn from experience, and dynamically adapt to new challenges. Agentic AI is responsible for designing, training, and optimizing these agents to complete tasks without human intervention.

While GenAI has already revolutionized Conversational AI by making virtual assistants more flexible and human-like, Agentic AI takes things further, introducing a deeper layer of intelligence and autonomy to these systems. This added intelligence manifests in key capabilities that set Agentic AI apart. These include:

• Autonomy – agents powered by Agentic AI can learn and operate independently, optimizing workflows and handling complex tasks without constant supervision.

• Decision-Making – these agents can assess situations and determine the best course of action, reducing reliance on human intervention.

• Problem-solving – Agentic AI follow a four-stage process: understanding, reasoning, acting, and learning. First, the agents collect and process data. Then, the Large Language Model (LLM) acts as an orchestrator, analyzing information to grasp the context. Finally, the AI interacts with external tools and continuously improves through feedback.

• External Environment Interaction – these agents proactively engage with their surroundings, gathering real-time data to make informed adjustments.

• Planning – they can handle complex scenarios and execute multi-step strategies to achieve specific goals, adapting as needed.

To better understand the value of Agentic AI, comparing it with Traditional and Generative AI within the Conversational AI landscape proves insightful.

To fully understand the concept of AI Agents, it is important to examine its features and technical capabilities from an academic perspective. Research in this field is still evolving, seeking to uncover broader implications for real-world applications of Agentic AI-powered agents.

One key concept that aligns with the vision of Agentic AI and extends to AI in general is “agenticness”. This term refers to the level of autonomy and initiative displayed by AI systems. Traditionally, AI has been reactive, following predefined commands. However, Agentic AI shifts this paradigm by enabling systems to make independent decisions and dynamically adapt to their environment.

However, this autonomy raises important questions: What are the limits of intelligent agents? Could they develop some form of functional “consciousness”, even in a limited sense? While today’s AI systems do not possess consciousness in the human sense, their ability to reason across different contexts and learn from experience brings them closer to behaviors traditionally associated with cognition.

This becomes even more relevant when considering the concept of self-motivation – the ability of AI Agents to initiate actions without relying on human prompts. Unlike traditional AI models, which wait for instructions, these agents proactively analyze contexts, set objectives, and execute adaptive strategies to achieve their goals. While this may resemble human initiative, it is still rooted in advanced programming and machine learning models. .

The vast capabilities and numerous applications that stem from this new paradigm cannot be overlooked. Agentic AI represents more than just another technological advancement – it marks a fundamental shift in how we perceive AI and its role in the world.

One way to truly understand this shift and gain a broader perspective on the concept is by listening to the insights of those directly involved. At BOTSchool, we reached out to our AI specialists to explore how this new form of interaction will initially impact Conversational AI Platforms.

Ricardo Filipe, Head of Artificial Intelligence and Conversational Gen AI Products, takes a pragmatic view: “The introduction of AI Agents represents a big advancement for Conversational AI. The limitations of assistants that merely react to commands and follow predefined workflows are disappearing.” In Ricardo’s vision, this new paradigm will “transform the user experience, making it easier and closer to real-human interaction.”

For businesses looking to grow and automate their processes, Ricardo adds: “In the mid-term, we will see AI super-agents that are far more efficient and capable of handling processes that previously required multiple interactions and human supervision.”

Finally, Ricardo states, “As a low-code platform, BOTSchool democratizes access to AI technology, empowering any company to leverage innovation, streamline processes, and drive business growth.”

Sara Furão, responsible for driving the development of this technology within BOTSchool, also shares her perspective: “After GenAI and the accessibility it brought to Conversational AI, AI Agents are the future. They are set to change interactions between machines and humans. We are developing agents that can learn, optimize, and adapt to different situations without the need for contact reprogramming.”

For businesses that choose to embrace this technology early on, Sara highlights the potential benefits: “It can be a game-changer in reducing operational costs while still delivering a highly personalized customer experience.”

The Agentic AI framework is not just about automation; it’s about enabling AI systems to operate with true autonomy. Unlike traditional models that rely on predefined rules or GenAI that focuses on content creation, Agentic AI integrates several advanced components to enable decision-making, self-learning, and proactive execution.

At its core, the functioning of Agentic AI can be broken down into the following key stages:.

• Reasoning Engine – using a LLM or another advanced AI model orchestrates the reasoning process. Through the analysis of user input, the agent interprets the information, evaluates different strategies, and determines the optimal course of action.

• Tool Usage – LLMs together with external tools (such as Knowledge Bases, websites/URLs, APIs, and code scripts) have complete access to rich information and can perform complex tasks with more capabilities and fewer limitations.

• Memory Calling – the agent leverages previously processed data from various information sources to access relevant memories, ensuring it invokes the appropriate tools to complete the task.

The operational flow of an Intelligent Agent can be represented in four essential steps, as illustrated in the image below:

AI Agents represent a paradigm shift in how intelligent systems interact with the world. As a result, the future of Conversational AI will be deeply intertwined with the evolution of these tools. Traditional chatbots and virtual assistants will gradually move beyond reactive systems, transforming into intelligent, proactive agents capable of handling complex workflows, anticipating user needs, and driving business efficiency at an unprecedented scale.

The field is also moving toward standardization, supported by emerging protocols like the Model Context Protocol (MCP), which offers a unified framework for connecting AI models with diverse data sources and tools. These protocols will become increasingly common, empowering organizations with the flexibility to choose the best vendors for their needs while promoting best practices in AI Agent development.

While this transformation is already underway, businesses and organizations still early in their automation journey should begin by implementing foundational AI solutions. This will help them identify high-impact use cases tailored to their goals and audience. From there, they can gradually scale and evolve their tools, ultimately reaching the stage where they can leverage AI Agents to fully embrace this new era of artificial intelligence.