AI Agents vs. Everything AI: All The Definitions You'll Ever Need

Artificial intelligence is evolving fast, and with it comes growing complexity. Our team counted over 250 AI-related acronyms (yes, "AI" is one of them). So if you’re struggling to tell AI from AGI, LLM from GPT, or RAG from RLHF, you’re not alone.

And then there are the tools: chatbots, workflows, assistants—and now, AI agents.

But what does it all mean? And what’s the difference—if there’s any difference indeed!

AI paradigms only get more confusing the longer you spend researching them—speaking from experience. So let me present you the complete guide to understanding the difference between AI agents and other AI paradigms—the unpretentious edit: no unexplained jargon or annoying sales pitch.

Here’s what we’ll cover:

• A brief overview of Agentic AI and its use cases.
• Comparisons with other AI paradigms, from chatbots to workflow automation.
• A look at emerging trends.

Let’s dive in.

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Overview: Agentic AI vs. AI agents

In a nutshell, agentic AI refers to semi-autonomous systems that can perform tasks or make decisions mostly independently, based on prior context and training data, to achieve a set of predefined goals or objectives. But they ask for further human input if they don’t have enough information—so despite popular online discussions, they are not fully autonomous.

Unlike traditional AI models, which typically require much human input and oversight for decision-making, agentic AI can act and adapt to dynamic environments without constant intervention. This level of autonomy makes it particularly useful in complex, unpredictable situations where quick decisions or continuous actions are needed.

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What’s the difference between Agentic AI and AI agents?

• AI Agent: Refers broadly to any AI system designed to perform tasks autonomously, using tools like machine learning or predefined rules.

• Agentic AI: A more advanced subset of AI agents characterized by greater autonomy, context-awareness, and adaptability. Agentic AI can make decisions, learn from feedback, and self-improve without needing constant human oversight.

💡 In short, all agentic AI are AI agents, but not all AI agents are agentic. Agentic AI offers deeper adaptability and decision-making.

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Agentic AI in e-learning

In the context of e-learning, EdTech, and professional training, agentic AI offers powerful solutions for personalized and adaptive learning experiences. Key applications include:

• Personalized learning Paths: AI agents can assess learners' progress in real-time and adjust training content accordingly, ensuring that each learner receives the most relevant and effective material.
• Automated coaching and feedback: AI agents can provide personalized feedback, simulate coaching scenarios, and offer tailored advice based on learners’ needs, improving retention and performance.
• Adaptive assessment: Agentic AI can create dynamic, context-sensitive assessments that adjust in difficulty based on the learner's responses, enhancing test accuracy and relevance.
• Learner support: AI agents can function as 24/7 support, answering questions or providing guidance during self-paced courses, helping learners stay on track without human intervention.

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AI agents vs. other paradigms & tools: The comprehensive definition list

Understanding the distinctions between various AI paradigms is essential when selecting the right approach for your product. Below, we compare agentic AI and AI agents to several other common paradigms, highlighting key differences and providing insights into their functionalities.

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What is the difference between an AI Agent and a Chatbot?

Chatbots have gone through an impressive transformation over the last few years, evolving from chat flows with predefined questions and answers to AI-powered conversational bots, however, they still have their limitations.

AI chatbots operate based on predefined knowledgebases or scripts and respond to user inputs with limited flexibility. Although much more powerful and helpful than they used to be, they still lack the autonomy to make decisions or adapt to new situations without human intervention.

In e-learning, chatbots might be used to answer frequently asked questions or provide basic support, but they don't modify the learner's experience based on the individual’s progress—as AI agents would.

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What is the difference between an AI Agent and a Virtual Assistant?

AI assistants can only provide support by automating tasks and offering information, operating within predefined parameters. However, they require human input for complex decision-making and lack the autonomy of AI agents to act independently.

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What is the difference between an AI Agent and an AI Model/LLM?

An AI model is the foundational component that processes data and generates outputs based on patterns. An AI agent uses AI models (like GPT) to make decisions and perform tasks based on context and goals. Simply put, AI agents would not be possible without the AI models.  

A large language model (LLM) is a type of artificial intelligence that can understand, process, and generate human language. LLMs are trained on large amounts of data, such as articles, websites, and even user feedback, to learn language patterns. How much data? It is estimated that ChatGPT-4’s LLM was trained on 12 trillion tokens which is about 10 trillion words.  

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What is the difference between an AI Agent and Generative AI?

Generative AI focuses on creating new content, such as text or images, based on existing data. While it can produce outputs, it can’t do the research or make decisions regarding what content to produce. For example, generative AI could be used to create comprehensive learning materials, but an AI agent would go many steps beyond and adapt the content delivery to the learner's progress.

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What is the difference between an AI Agent and ChatGPT/GPT?

Is ChatGPT an AI agent? ChatGPT is one of the most popular large language models (LLM) that generates human-like text based on input. It comprises four key components: transformer architecture, tokens, context window, and neural network. But it isn’t an AI agent.

ChatGPT can provide helpful responses in a training environment, but an AI agent might offer personalized learning paths based on a learner's interactions and goals. Not to mention that AI agent builders specializing in L&D heavily invest in data security and intellectual property (IP) protection, so you can rest assured that your company data will not end up encrypted in an LLM’s training data.

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What is the difference between an AI Agent and Copilots?

Copilots support users by providing recommendations, automating tasks, or assisting decision-making but rely on human input to act. In this sense, it’s more similar to an AI assistant. Copilots enhance human efforts and can support AI agents—while AI agents take autonomous actions to achieve outcomes.

Copilots have been gaining popularity in e-learning and L&D due to their ability to process information and provide support, however, they are made for generalist use cases and aren’t tailored for the needs of e-learning providers. AI agents are built to execute EdTech-specific tasks and they can align not only with the user’s industry and use case but their specific business case.

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What is the difference between an AI Agent and Workflow Automation?

Workflows automate predefined tasks based on set rules. Although workflows are often able to carry out actions across multiple, integrated platforms, they lack the adaptability and decision-making capabilities of an AI agent.

Automated workflows have been around for a long time and they are a great way to save time on repeating tasks. Workflows are usually triggered by specific events or rules and run a set of predetermined actions. Although workflow actions can be skipped or adjusted based on predefined rules, they lack the robust adaptivity of AI agents.

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What is the difference between an AI Agent and a Bot?

A bot is a software application designed to automate simple, repetitive tasks without human intervention. Bots (task-oriented AI) operate based on predefined rules and lack the ability to learn or adapt beyond their initial programming.

In contrast, an AI agent possesses the capability to learn from interactions, adapt to new information, and make autonomous decisions. AI agents utilize advanced algorithms and machine learning to handle complex tasks, exhibiting a level of flexibility that bots do not.

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What is the difference between an AI Agent and Robotic Process Automation (RPA)?

Robotic Process Automation (RPA) involves the use of software to automate highly structured, repetitive tasks typically performed by humans. These tasks follow clear, predefined rules and involve structured data. RPA lacks the ability to handle unstructured data or make decisions beyond its programming.

In a professional training environment, RPA could automate the enrollment process for new courses, ensuring that all necessary administrative steps are completed efficiently. An AI agent, on the other hand, could assess an employee's current skill set, recommend relevant training programs, and even tailor the learning experience to the individual's learning style.

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What is the difference between Agentic AI and Multi-Agent Systems?

Multi-agent systems involve not one but multiple agents working together to achieve a common goal. Each agent operates autonomously, but the system as a whole requires coordination.

In an EdTech platform, multiple agents might work together to assess learner performance, suggest learning paths, and evaluate content quality, but each agent acts independently and within the scope of their training.

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What is the difference between Agentic AI and Compound AI?

Compound AI integrates multiple AI models or systems to perform complex tasks. It may not possess the autonomy of an agentic system but combines capabilities to enhance results. For example, a compound AI system might combine generative AI for course content creation with workflow automation for content delivery for learners.

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What is the difference between an AI Agent and Causal AI?

Causal AI focuses on uncovering cause-and-effect relationships within data, enabling better predictions and decisions. For example, causal AI can identify the key factors that lead to learner success and the purchase of e-learning memberships.

Causal AI provides the "why" behind outcomes; AI agents act on that understanding to achieve goals autonomously.

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What is the difference between an AI Agent and RAG?

Retrieval-Augmented Generation or RAG combines information retrieval with generative capabilities to produce more accurate outputs.

RAGs, like NotebookLM, are popular with learners as they can upload course materials (text, PDF, and even video) into the platform and generate summaries, papers, and other content based only on the provided sources. An AI agent would use that content to adapt an entire learning experience based on real-time learner feedback.

When to use Agentic RAG

Agentic RAG combines the autonomous decision-making of agentic AI with the information retrieval capabilities of RAG, making it suitable for applications requiring both autonomy and access to external knowledge bases—this is also what makes Mindset AI so special and popular.

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Other agentic systems: Workflow & GUI agents

You can make architectural distinctions between different agentic systems. Each system's approach has pros and cons. And eventually, they will all blend into one anyway. Let’s dig into these two agentic systems:

• Workflows are systems where LLMs and tools are orchestrated through predefined steps. These steps enable agents to call an API and pass data from one system to another within the workflow.
• Computer GUI systems where LLMs (using AI vision) take screenshots of a user interface to assist in decision-making, dictate their own processes, and use tools that make sense. They are in full control over how that task is completed.  

Multiple levels of capabilities can be used when it comes to both of those systems. For example:

• Augmented LLM
• Prompt chaining
• Evaluator-optimizer
• Orchestrator-workers
• Parallelization
  • Sectioning: Breaking a task into independent subtasks run in parallel.
  • Voting: Running the same task multiple times to get diverse outputs.

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Workflow vs. GUI agent

When deciding between using a workflow or a GUI agent, it's crucial to consider the nature of the task, the desired level of autonomy, the complexity of the interface, and the trade-offs between cost, efficiency, and accuracy.

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Workflow agents

Workflow agents excel in situations where tasks can be broken down into well-defined, sequential steps with clear decision points and predictable outcomes. They are highly effective for automating structured processes that involve interacting with APIs, databases, or other back-end systems.

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GUI agents

GUI agents are designed to interact directly with Graphical User Interfaces—hence the name—, mimicking human actions to automate tasks that typically require manual input. They are particularly useful for automating tasks within applications that lack APIs or have complex interfaces that are difficult to access programmatically.

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In summary, both Workflow and GUI agents have advantages and disadvantages:

Workflow agents

Advantages:

• Predictability and consistency
• Scalability and efficiency
• Transparency and auditability

Limitations:

• Lack of Flexibility
• Difficulty with Complex Interfaces
• Potential for Bottlenecks

GUI agents

Advantages:

• Flexibility and adaptability
• Ability to automate complex tasks
• Potential for end-to-end automation

Limitations:

• Complexity and development costs
• Sensitivity to interface changes
• Potential for errors
• Limited transparency

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Future Trends in AI: Collaboration Between Humans and Machines

As AI technology advances, there are endless opportunities; A future where humans and AI don’t compete but collaborate—each amplifying the other’s strengths. In fields like e-learning and L&D, this partnership promises to revolutionize how we learn, work, and grow.

• Augmenting human work: AI agents will redefine how humans do their work and allow people to delegate unwanted or complex tasks to their AI coworkers. We will see the emergence of new skills like AI agent–human worker relationship management and agent workflow maintenance.
• The rise of multi-agent systems: We will see different agents work together across systems to achieve their common goals—of course, this will also require significant advancements in interoperability and design.
• The fall of traditional SaaS giants: Specialized AI agents will replace generalist software tools (like ERPs & CRMs) that come with high costs and maintenance time commitment. AI agents will do a better job at specialized tasks for the same or lower investment.

As AI becomes more autonomous, ethical design becomes more crucial. Together, technologists, ethicists, and users can ensure AI advances responsibly, aligning with shared values and goals. The future isn’t about AI replacing humans—it’s about unlocking potential together.

For a deep dive on future trends in agentic AI, read our AI experts' predictions here.

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