Agentic AI 101: Everything You Ever Wanted To Know About AI Agents But Never Dared Ask

What is agentic AI? If you're picturing AI agents that brew your coffee, run your dishwasher, analyze user behavior on your e-learning platform, optimize learning paths, and then top it all off by writing a viral LinkedIn post about the future of L&D—completely autonomously… well, you might want to lower your expectations.

We’re not quite there—yet.

OpenAI recently introduced its first AI agent, Operator, and people are *not* impressed. There’s been so much hype and discussion about agents that the much-anticipated release couldn’t possibly live up to the expectations.

Operator and other “young” AI agents make mistakes even, even with simple, predictable tasks. there’s always a learning curve. Early LLMs were notorious for hallucinations,, but now they can cite sources and even correct their own errors.. And I bet, the first time you loaded your dishwasher, you put forks and knives in the tray pokey side up. But we learn.  

This article was created in collaboration with AI experts and Product Owners, using real-life examples from EdTech leaders who are already utilizing agentic AI. It covers everything you need to know about where AI agents stand today—and where they’re headed. Consider this your Agentic AI 101.

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What is an AI agent in simple words?

Right now, AI agents are being defined in all sorts of ways

Some see them as fully autonomous systems that operate independently over extended periods of time, using various tools (more on this later), so they can achieve an objective or complete a task. Others describe a more structured approach, where workflows guide their actions.

At Mindset, we define all of these as agentic systems. AI agents are semi-autonomous systems that can use other tools and operate on their own as long as they have enough data and context—and can identify when to ask for more input.

We’ve covered this topic in a recent episode of In The Loop. Listen to the podcast to learn more about agentic systems.

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Examples of agentic systems

Let's break it down simply. Much of the content on this topic is unclear, vague, and often written without real experience. Let’s look at some examples to make this topic less abstract.

Agentic systems distinguish themselves from typical RPA (Robotic Process Automation) by their ability to operate semi-autonomously to achieve an objective.

For example, say you create a workflow to complete an RFP (Request For Proposal) for a prospect. It might:

→ extract data from separate spreadsheets

→ compile it for a human to confirm

→ collect more info from the human

→ complete a set of RFP questions.

Non-agentic systems need to go through each step, collect data, and check in with a human constantly. No ability to ‘operate semi-autonomously to achieve an objective’.

With an agentic system, the LLM—through chain of thought and other prompting approaches—can establish whether it already has the necessary information (data and process knowledge) to complete the RFP, and therefore, doesn’t need to ask a human for more context.

As agents gather more data and refine their understanding of processes, they become more complex—but also more reliable. At scale, agentic systems become extremely powerful and helpful. As a result of these efficiencies, memory, tools, and reasoning (clever prompting), the performance of the agents is much better than traditional chatbots or robotic process automation systems.

Another example: while traditional chatbots—even those powered by advanced models like GPT-3.5—might achieve around 48% accuracy in coding tasks, AI agents utilizing the same model can boost correct answers to an impressive 95%. This demonstrates their superior capacity for problem-solving and task completion.

Want to know how we got here? Read about the history and evolution of agentic AI.  AI agents didn't just appear overnight—they evolved from decades of research in artificial intelligence and robotics. From the first rule-based systems in the 1960s to today's sophisticated LLM-powered agents, it's been quite a ride.

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What is the main task of an AI agent?

So what's the main goal of AI agents? Simply put, AI agents exist to help you get stuff done—specifically, to handle complex tasks that require planning and multiple steps. Think of them as very eager but sometimes confused assistants who can use tools and remember context. (A step up from your typical chatbot that forgets what you—or even they—said two messages ago).

The core purpose of an AI agent is to:

• Understand and work toward specific goals independently
• Make decisions about what steps to take
• Use available tools and information effectively
• Learn from feedback and adapt its approach

An agent's main task is to turn your high-level instructions into concrete actions. For example, when you tell a human assistant to "prepare the quarterly report," they know to gather data, create charts, write analysis, and format everything - without you spelling out each step. AI agents aim to work the same way. They break down big goals into smaller tasks and figure out how to complete them using their available tools and capabilities.

The key difference from regular AI systems? Agents don't just respond to prompts: they actively work toward objectives. Sometimes they succeed brilliantly, sometimes they fail spectacularly, but they're always trying to figure out the next logical step. Kind of like that new intern who has great initiative but occasionally needs a gentle course correction.

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Understanding the architecture of an AI agent

While a typical AI model responds to a single prompt with a single answer, an AI agent operates at a higher level. It can interpret a goal, reason and plan to break it into actionable steps, reflect on its progress, and follow workflows to execute those steps. It utilizes tools, data sources, and APIs to complete tasks along the way.

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Core Principles of AI Agents

Continuous Reasoning and Planning

Instead of simply responding to a single prompt, AI agents work in iterative loops. They don’t just generate text; they plan and execute actions to complete a task.

For example, if an agent’s goal is to locate a file, it will identify what to search for, review its plan, verify specific details, and then deliver the file. This process can become more sophisticated, including reflection steps where the agent evaluates its actions, and adjusts to improve future tasks.

Memory and context retention

AI agents can use memory to track past instructions, and user preferences, and gather information. This memory can be procedural, storing best practices learned from user corrections, or personal, retaining user-specific details for a more tailored experience. For instance, if you correct an agent once, it can remember that correction and avoid repeating the mistake in the future.

Tool use and external integration

What sets agents apart is their ability to interact with external tools and APIs. Instead of providing text-based answers, they can retrieve or update data in external systems. For example, an AI agent might update a lead's status in a CRM, log onboarding completion in an HRIS, or send reminders via WhatsApp.

Adaptive user experience and interactivity

AI agents are not isolated systems that operate without human input. They can involve users at critical decision points or gather key data through chat, enabling a human-in-the-loop approach. This allows users to pause, adjust, or guide the agent’s actions. The challenge is finding the right balance: too much oversight can make the agent redundant, while too little can lead to errors or misalignment.

Workflow (capability) building

Large Language Models (LLMs) are powerful but have limitations. Agents overcome these challenges through "flow engineering"—structuring their logic into clear, step-by-step processes. For example, a complex coding task mbght include a planning phase, code generation, testing, and verification. Defining and guiding the agent through these steps ensures more consistent and reliable results.

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How do AI agents work?

Think of an AI agent as a very methodical colleague who follows a "think-act-reflect" cycle. Here's what happens under the hood:

Planning phase

• The agent receives a goal ("organize these sales reports")
• It breaks this down into smaller tasks
• It decides which tools or APIs it needs

Execution phase

• The agent runs each step in the sequence
• It checks if it has the right information
• It uses its tools (maybe pulls data from Salesforce)
• Then it adjusts its plan if something doesn't work

Reflection phase

• Evaluates if the output matches the goal
• Learns from what worked (or didn't)
• Decides if it needs to loop back or move forward

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The Four Fundamental Rules of AI Agents

The four main rules of AI agents aren't just guidelines: they are what separates a true AI agent from a fancy chatbot. We call them the PART principles.

1. ‍Purposeful Action
   ‍The agent must understand and actively work toward specific goals. No random responses or generic chit-chat. Everything it does should move it closer to completing its task.
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2. ‍Autonomous Decision-Making
   ‍The agent decides its next steps without constant human input. It's like having an employee who doesn't need step-by-step instructions for every little thing. (Though like any good employee, it should know when to ask for help.)
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3. Resource Awareness
   ‍The agent must know what tools it has access to and when to use them. This includes APIs, databases, external services—even its own memory of past interactions. It's no use having a Swiss Army knife if you don't know which blade to use.
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4. Task Completion Tracking
   ‍The agent monitors its progress and knows when it has (or hasn't) achieved its goal. This means understanding both success and failure conditions—and being honest about which one it's reached.

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These rules might sound simple, but they are what make AI agents genuinely useful for real-world tasks. An agent that follows these rules won't just give you information: it'll help you get things done.

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What’s the difference between horizontal, vertical, narrow agents?

There are so many ways to slice and dice AI agents. We’ve covered the five common types and in the next section we’ll please the geekiest of you with a deep dive into Workflow and GUI agents—but there’s another categorization that is gaining popularity.  

Let’s explore the definitions and differences between horizontal, vertical, and narrow agents.

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Horizontal AI agents

Horizontal agents work across different industries and use cases. They can handle general tasks like scheduling, email management, or data entry and they do a fine job—as long as the task doesn’t require specialized, deep training.

Think of them as administrative assistants who can work in any department. They are great, hardworking employees who can work across the board but their knowledge is not deep enough for specialist tasks. They could, probably, write a marketing email but the results would likely reflect their lack of specialized skills.

Example: An agent that can handle calendar management for any type of business.

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Vertical AI agents

Vertical agents specialize in specific industries or functions. They have deep knowledge of particular domains since their training data is based on that very domain.

Think of an experienced Product Owner of an LMS (Learning Management System). They have endless knowledge of designing roadmaps, impressive project management skills, and visibility of intellectual property protection considerations—but you wouldn’t ask them to call prospective buyers, would you?

Example: Mindset AI focuses on agentic solutions for e-learning platforms. Our clients use the tool to deploy AI agents that help guide learners through pathways, improve engagement, and as a result, increase course enrollments—by as much as 900%—and improve platform KPIs like membership retention and revenue per user. You could, technically, use Mindset AI to answer frequently asked questions for a dental clinic—but that’s not where it shines.

Vertical agents will significantly change the landscape of SaaS by offering more specialized solutions. How this transition might look and why SaaS providers need to pay attention? Listen to In The Loop to find out more about the rise of vertical AI agents.

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Narrow AI agents

Despite what sci-fi movies suggest, today's most effective agents are actually narrow AI agents. Nowadays, with all the hype around agentic AI, when tech companies claim to have AI agents, they mean narrow agents—or not even that, just chatbots. But narrow agents are fantastic!

They're specialists, not generalists:

• Focus on specific, well-defined tasks
• Excel within clear boundaries
• Have deep but limited capabilities
• Work best when their scope is clearly defined

Think of narrow agents like specialized tools in your toolbox. You wouldn't use a screwdriver to hammer a nail—at least not very successfully. Similarly, narrow agents are most effective when used for their intended purpose. The trick isn't finding one agent to do everything, but knowing which agent to use for each task.

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Mindset AI is a vertical AI agent platform as a service designed specifically for e-learning and EdTech. Developed over years with direct input from industry leaders, our agents are designed to excel across a range of use cases in this space.

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What types of agentic systems are there? (For The Technical Reader)

Let’s geek out a little and dig into the technicalities of agentic systems and architectures.

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. All that matters is that an agent fetches data, actions it, and turns it into a new output. Critically, this will often require the agent to be able to call an API. The two most common right now are:

• ‍Workflows: Systems where LLMs and tools are orchestrated through predefined steps.
• Computer GUI: LLMs (using AI vision) take screenshots of a user interface to assist in decision-making, establish processes, and use relevant tools.

Multiple levels of capabilities can be used when it comes to both of those systems. Below is a handful of approaches that you can take.

Augmented LLM: The basic building block of agentic systems is an LLM enhanced with augmentations such as retrieval, tools, and memory.

Prompt chaining: Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. You can add programmatic checks (see "gate” in the diagram below) on any intermediate steps to ensure the process is still on track.

Evaluator-optimizer: In the orchestrator-workers workflow, a central LLM breaks down tasks, delegates them to worker LLMs, and synthesizes their results.

Orchestrator-workers: In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop.

Parallelization: LLMs can sometimes work simultaneously on a task and have their outputs aggregated programmatically. This workflow, parallelization, manifests in two key variations:

• 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 or GUI agent: How to choose

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.

Both approaches offer distinct advantages and limitations:

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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.

Advantages:

• Predictability and consistency: Workflows deliver more consistent results and minimize unexpected behavior, particularly when handling sensitive data or critical processes
• Scalability and efficiency: Workflows can scale to handle high volumes of tasks and can be optimized for speed and efficiency.
• Transparency and auditability: The structured nature of workflows makes it easier to track progress, identify errors, and ensure compliance with regulations.

Limitations:

• Lack of flexibility: Workflows struggle with tasks that require dynamic adaptation, handling exceptions, or responding to unpredictable events (until Multi-Agent workflow are safely deployed at scale).
• Difficulty with complex interfaces: Interacting with GUIs that have dynamic elements, complex layouts, or require nuanced human-like interactions can be challenging for workflow agents.
• Potential for bottlenecks: If a single step in the workflow fails, it can halt the entire process, requiring manual intervention or sophisticated error-handling mechanisms.

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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.

Advantages:

• Flexibility and adaptability: GUI agents can handle dynamic interfaces, adapt to changes in layout or functionality, and even learn from user interactions to improve their performance.
• Ability to automate complex tasks: GUI agents can perform tasks that involve multiple steps, data entry, navigation through menus, and even interacting with visual elements like buttons or drag-and-drop interfaces.
• Potential for end-to-end automation: GUI agents can bridge the gap between different applications and systems, enabling automation of entire workflows that involve multiple GUIs.

Limitations:

• Complexity and development costs: Building GUI agents requires specialized expertise in computer vision, natural language processing, and interaction design, making them more expensive to develop and maintain.
• Sensitivity to interface changes: GUI agents are highly dependent on the specific layout and functionality of the target interface. Even minor changes can disrupt their operation, requiring frequent updates and adjustments.
• Potential for errors: Accurately recognizing and interacting with GUI elements can be challenging, particularly with complex or dynamic interfaces, increasing the risk of errors or unintended consequences.
• Limited transparency: Understanding the decision-making process of GUI agents can be difficult, making it challenging to debug errors or ensure compliance with regulations.

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In short…

Use a workflow agent when:

• The task involves well-defined, sequential steps with predictable outcomes.
• The process can be easily automated using APIs or other programmatic interfaces.
• Consistency, reliability, and auditability are paramount.
• Cost-effectiveness and ease of implementation are your priorities.

Use a GUI agent when:

• The task requires interacting with a complex or dynamic GUI that lacks APIs.
• The process involves multiple complex steps and you struggle to define what success looks like.
• Flexibility and adaptability are crucial for handling evolving interfaces.

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Other comparable AI paradigms

We’ve been through so many AI agent types and different categorization options. Is the hype around AI agents starting to make sense? No? Perhaps comparing AI agents with somewhat similar or easy-to-mix-up categories will help unblur the picture!

While chatbots respond to messages, RAG systems retrieve information, and standard LLMs generate content, agents actively and semi-autonomously work to accomplish goals. They can use these other technologies as tools: an agent might employ RAG to find accurate information, use an LLM to process it, and feed a chatbot with the surfaced information.

Check out our definition library where we compare AI agents to chatbots, workflows, virtual assistants, LLMs, RAG, and more. Plus, take our AI agent quiz to test your knowledge.

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How are AI agents trained?

Here's a common misconception: many people think that AI agents are “trained” the same way as machine learning models—feeding them lots of data until they learn patterns. The reality is quite different.

Developing an AI agent is more like hiring and onboarding a new employee. You don't train them from scratch; instead, you configure their role, give them the right tools, and set clear expectations—or scope. The underlying LLM provides the basic capabilities, but you need to structure how they use these capabilities.

For example, when creating an agent to help with course assessments, you'd:

• Define exactly what makes a good assessment question
• Give it access to your course materials and learning objectives
• Set up rules for difficulty levels and question types
• Create processes for reviewing and refining its output

The development process often surprises people with its iterative nature. Take Mindset AI’s agents, for example. It uses all the major LLMs: GPT-4o, Gemini, Claude, and Llama—so it’s already trained on those models. You don’t need to spend time and money on model training, you can skip this step and start with content ingestion and limiting which agent has access to what content.

For instance, Achieve Unite had a vast library of books, resources, learning materials, and assessment tools but they struggled to package and deliver all this content in a way that was easy to consume for their members. First, they started with just one Mindset AI agent, but today they have multiple subject matter expert AI agents to support their users. As a result, they reported 40% faster partner onboarding and enablement, as well as a 20% increase in retention. Read Achieve Unite’s success story.

The key lesson? Start small and build up. Many organizations fail by trying to create the perfect, all-powerful agent from day one. Instead, think of it as developing a junior team member—give them simple tasks first, monitor their performance, and gradually increase their responsibilities as they prove themselves.

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Benefits, risks, and challenges

AI agents can transform how educational platforms deliver personalized learning experiences—but they're not magic. They can reduce manual work, scale personalized support, and handle complex workflows. However, they also require careful implementation, ongoing oversight, and clear boundaries. We've analyzed all the key considerations you need to know before implementing agents in your learning platform.

Read this interview with our in-house AI & ML experts about the reality of agentic AI: the benefits, drawbacks, and risks of AI agents.

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Conclusion: Are AI agents just hype?

The emergence of AI agents has sparked a wave of excitement, but it's crucial to separate genuine potential from inflated expectations. While some may view agents as fully autonomous systems operating independently for extended periods, others see them as more prescriptive implementations adhering to predefined workflows.

The perception of "hype" often hinges on expectations. If the expectation is for completely autonomous systems to replace human involvement entirely, that’s far from reality. However, when viewed as powerful tools—or even coworkers—that can automate complex tasks, enhance productivity, and augment human capabilities, AI agents offer lots of value and will improve rapidly.

What’s next for agentic Ai? What will artificial intelligence do in a hundred years? Who knows! But there are some emerging trends that are shaping at least the next few years. Read this article with some AI predictions from our Chief Product Officer.

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