What Are LLM AI Agents and What You Need to Know About Them

By Martin Brossman & Dr. Justin B. Rose, CWDP with contributions from David Amerland 🇺🇦 and Dana Gower MBA CCP®

Artificial Intelligence (AI) agents are transforming how we interact with technology by taking on tasks that once seemed exclusively human. At their core, these agents are designed to sense, decide, and act autonomously within their environment, continuously learning and adapting as they go. They leverage advanced models like large language models (LLMs), which draw on vast amounts of data to generate human-like decisions and behaviors. As we often emphasize in our training, these agents represent a leap beyond static programming, offering a dynamic, responsive approach to problem-solving that spans applications from personal assistants to complex industry automation. We must understand not just the capabilities of these agents but also how to guide their development responsibly and effectively.

 “AI Agents handle tasks that require decision-making, real-world interactions, and autonomy." - David Amerland

AI Agents vs. Simple LLM Prompts

The difference between an AI agent and just using a large language model (LLM) with a prompt boils down to autonomy, adaptability, and task execution. When you use an LLM, it’s all about responding to the prompts you provide—one input, one output. On the other hand, an AI agent operates independently, carrying out tasks, making decisions, and refining its actions based on goals and feedback.

AI agents use LLMs as part of their toolkit but take things further by dynamically creating prompts, interpreting responses, and integrating those insights into a broader action plan. For example, imagine an AI agent handling customer support. It might ask the LLM to summarize a customer’s issue, generate possible solutions, and decide on the next best step, like resetting a password or escalating to a live agent, all while adapting to feedback.

This is where the magic happens: the agent’s ability to operate independently, maintain context, and execute a series of steps without constant human input. It’s not just answering questions; it’s working toward results, and that’s a game-changer.

AI Agents cost a lot less to develop (usually in the range of $50,000 to $150,000) and are a lot cheaper to run with their cost per query running to less than one-tenth of that of chatGPT.  - David Amerland 

RAG’s Role in Shaping Advanced AI Agents

But how do AI agents actually achieve this level of autonomy and effectiveness? The answer lies in Retrieval-Augmented Generation (RAG), a foundational framework that bridges the gap between static knowledge and dynamic action. Static knowledge provides AI with a solid foundation of pre-learned information, but it’s limited to what existed at the time of training. Dynamic action, on the other hand, allows AI to retrieve and apply real-time, context-specific information, making it adaptable and responsive to ever-changing situations. RAG empowers AI agents to combine these two strengths, retrieving and processing up-to-date, relevant data from external sources like databases, APIs, or documents.

It’s worth noting that not all RAG implementations rely on live data streams. Some systems use cached or semi-static retrieval databases, ensuring efficiency while maintaining relevance. Regardless of the specific implementation, RAG equips AI agents with the tools to ground their outputs in reliable information and operate more intelligently.

How Does RAG Work?

Building on this foundation, Agentic RAG takes things to the next level. While traditional RAG typically responds to user queries, Agentic RAG empowers AI agents with active decision-making. These agents don’t just wait for instructions but they autonomously determine when to retrieve information, which data sources to use, and how to integrate insights into their actions. They adapt to feedback, can orchestrate complex workflows, and solve multi-step problems independently, transitioning from reactive tools to dynamic problem solvers.

Understanding this progression from basic RAG to its agentic evolution is key to appreciating how AI agents achieve their advanced capabilities. It’s like watching a puzzle come together step by step, where each stage builds toward something more powerful. 

When you think about Retrieval-Augmented Generation (RAG) and how it helps AI agents become smarter, it’s a lot like solving puzzles. Imagine sitting down with a new box of pieces. You start by sorting the edges, grouping similar colors, and figuring out where to begin. Each stage feels a little more complex, but it’s always satisfying when the pieces start to fit together.

That’s how RAG works, moving step by step from simple tools to something much more powerful. At first, there’s Naive RAG, where it’s as if you’re grabbing random pieces and trying to build the frame. The AI pulls chunks of information and tries to make sense of them. It works well enough for straightforward tasks like answering FAQs or summarizing a single document. But it can easily pick the wrong pieces, leaving gaps or blurry spots in the picture irrelevant information, factual errors, or unclear outputs (hallucinations).

Next, you move to Advanced RAG, like when you’ve learned to tackle the puzzle with better strategies. You start grouping by patterns or focusing on one tricky section at a time. At this stage, the AI gets smarter about what it retrieves and how it organizes it. Improvements like query optimization help it find better pieces, while tools like context compression, focusing the topic, ensure the final result fits together neatly. Suddenly, it’s solving more nuanced, multi-step problems and producing clearer, more polished responses.

As the puzzle grows more complicated, you shift to Modular RAG, where each section tells a smaller story within the bigger picture. Here, the system divides itself into parts, each focusing on a specific task. It’s like having a separate pile for the sky, the forest, and the mountains where everything works in harmony. With this modular approach, the AI becomes more flexible, easier to tweak, and ready to take on bigger challenges.

And finally, there’s Agentic RAG, the stage where you’re not just solving puzzles anymore by directing the AI. Now, the AI acts more like a teammate, proactively deciding which pieces are missing, where to find them, and how to fit them in. It’s no longer waiting for you to direct every step; it’s working independently to build something new and dynamic. Imagine an agent handling customer support. It doesn’t just pull pre-written answers but it tailors its responses in real time, factoring in live updates or unique customer needs. Or think of it monitoring market trends, retrieving critical data, and crafting actionable insights without needing constant input.

Ultimately, RAG transforms AI agents into intelligent partners who do not just provide answers but solve problems, adapt to new challenges, and proactively make decisions. Whether it’s responding to unpredictable customer needs or analyzing rapidly changing data, RAG ensures AI agents stay relevant, reliable, and ready for the complexities of the real world. It’s the engine behind their evolution from reactive tools to proactive, autonomous problem solvers. This also presents the possibility of various types of AI Agents capable of performing different tasks. 

Types of AI Agents 

Understanding the different types of AI agents can help us grasp how these technologies are designed to function in various environments. Each type has distinct characteristics and applications, which are crucial to their effectiveness. Here’s a breakdown of AI agents, explained in a straightforward way:

Tool Agents

• These agents specialize in interacting with external tools and databases to execute tasks. For example, they might use an external planner to optimize logistics or query a database to retrieve specific information.

• They focus on narrow, well-defined tasks like answering questions or automating parts of business processes.

Game Agents

• Designed to simulate game players, these agents operate in environments like video games or simulations.

• They use memory, planning, and decision-making to navigate complex, dynamic scenarios. Think of them as AI-driven gamers capable of strategizing and adapting on the fly.

Simulation Agents

• These agents function in simulated environments, often replicating real-world scenarios to test systems or train AI models.

• They are highly dynamic, learning from ongoing feedback to refine their actions and adapt to changes in the simulated world.

Web Agents

• These agents are focused on navigating and interacting with online environments. They retrieve, analyze, and act on web-based information.

• They might be used for tasks like market research, content aggregation, or automating online interactions.

Assistant Agents

• These are the helpers many of us are familiar with, like virtual assistants or chatbots.

• They excel in personal productivity, answering questions, scheduling tasks, and even providing recommendations.

Generative Agents

• These agents are designed to create new content, from writing and art to music and code.

• Leveraging generative AI technologies, they push the boundaries of creativity by combining patterns and learning from vast datasets.

Embodied Agents

• These agents exist within physical or virtual bodies, interacting directly with their environment.

• Robots or virtual characters in games are good examples, as they engage with their surroundings using sensors, memory, and planned actions.

Each of these agent types plays a role in reshaping how AI integrates into our lives. By understanding their unique capabilities, we can better harness their potential while navigating their limitations.

Aspects to consider with AI Agents. 

When integrating AI agents into our workflows, it's essential to approach their implementation thoughtfully to maximize benefits and mitigate potential challenges. Here are key considerations to keep in mind:

Define Clear Objectives

• Establish specific goals for your AI agent to ensure it addresses your unique needs effectively

Data Quality and Management

• Ensure your AI agent has access to accurate, relevant, and up-to-date data to function optimally.

Ethical and Legal Compliance

• Adhere to ethical standards and legal regulations to maintain user trust and avoid potential liabilities.

User Training and Engagement

• Educate your team on the AI agent's capabilities and limitations to promote effective collaboration.

Continuous Monitoring and Improvement

• Regularly assess the AI agent's performance and make necessary adjustments to enhance its effectiveness.

Focusing on these considerations can help you integrate AI agents into your operations effectively and responsibly.

Advanced Cognitive Features of AI Agents

AI agents are distinguished by their ability to combine various advanced cognitive features, making them more adaptable and human-like. These features include:

• Memory Systems: Agents use short-term memory for immediate context and long-term memory to recall past interactions, enabling continuity in multi-turn conversations and dynamic environments.

• Reasoning Frameworks: Techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) allow agents to solve complex tasks by breaking them into manageable steps or exploring multiple solutions in parallel.

• Tool and Extension Integration: Tools such as APIs and plugins extend the agent’s ability to interact with the outside world, like fetching real-time data or managing third-party systems dynamically.

• Feedback-Driven Learning: Agents refine their decisions using feedback from environments, humans, or internal models, allowing them to adapt and improve over time.

Core Functionalities of AI Agents: Profile, Memory, and Action

To better understand the inner workings of AI agents, it’s essential to explore three foundational aspects—Profile, Memory, and Action. Each plays a vital role in how agents operate and achieve their goals:

Profile:

• Shapes the agent’s role, goals, and behavior.

• Includes demographic (e.g., age, location), psychological (e.g., personality traits), and contextual data (e.g., expertise in a subject area).

• Enables agents to adapt to user-specific needs or scenarios, enhancing personalization.

Memory:

• Provides a framework for agents to store and retrieve information from past interactions.

• Short-term memory focuses on immediate tasks, while long-term memory accumulates knowledge over time.

• Supports consistency, learning, and improved decision-making in dynamic environments.

Planning:

• Breaks tasks into manageable steps using reasoning strategies like single-path or multi-path approaches.

• Incorporates feedback from environments, humans, or internal mechanisms for dynamic plan refinement.

• Adapts plans to achieve goals effectively in complex and evolving scenarios.

Action:

• Executes decisions through external tools, APIs, or other methods.

• Allows agents to complete tasks, solve problems, and interact with their environment dynamically.

• The effectiveness of actions depends on the integration of memory and planning mechanisms.

By understanding these core functionalities, users can appreciate how AI agents simulate human-like processes to deliver dynamic, context-aware solutions.

“AI Agents are made up of four distinct layers: Input, processing, action, learning.” - David Amerland

A few Real-World Examples of AI Agents

Customer Service Chatbots (e.g., WebGPT, HuggingGPT):

• AI agents like WebGPT autonomously fetch relevant data from external systems to answer customer queries accurately and handle complex support scenarios.

Travel Booking Agents:

• Agents such as Google Flights Extension dynamically book travel by parsing user queries and making API calls to fetch and reserve tickets.

Smart Home Management:

• AI agents autonomously control smart home devices by interacting with external tools like IoT APIs, adjusting settings, and optimizing energy consumption based on contextual feedback.

Game Development and Strategy Assistance (e.g., Voyager):

• Agents simulate behaviors in games or assist in strategy planning by using memory and iterative feedback from the game environment.

What to read next? The Future is Agentic by David Amerland https://davidamerland.com/seo-blog/1468-the-future-is-agentic.html

"What makes AI Agents exciting, however, are two distinct capabilities: first, the ability to query LLMs and take advantage of their text and image generative capacity, and second, their ability to learn from experience so that they become more personalized and productive in their environment.” - David Amerland

With AI agents, it’s essential to remember that the human element remains irreplaceable. While these tools can handle complex tasks, adapt dynamically, and even simulate creativity, their outputs depend on the integrity, judgment, and guidance of people. By staying engaged—whether through defining objectives, verifying outcomes, or fine-tuning applications—we ensure that AI serves as a powerful ally, not a runaway system. This partnership between human insight and machine capability is what allows us to innovate responsibly, amplify our strengths, and stay grounded in the values that technology should ultimately uphold: enhancing lives and fostering connection. Let’s not just adopt AI—let’s lead it.

What to read next? The Future is Agentic by David Amerland https://davidamerland.com/seo-blog/1468-the-future-is-agentic.html

Reference: The following two resources contributed to the development of this article:

A Survey on Large Language Model-based Autonomous Agents https://arxiv.org/pdf/2308.11432 

From Google: Agents Authors: Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic https://ia800601.us.archive.org/15/items/google-ai-agents-whitepaper/Newwhitepaper_Agents.pdf 

Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG https://arxiv.org/abs/2501.09136?utm_source=chatgpt.com 

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